Intersections of Technological and Regulatory Zones in Regenerative Medicine

  • Linda F. Hogle


This chapter situates contemporary debates over regenerative medicine governance within a broader framework, taking intersections with economic, political, and other kinds of technological zones into account. With the inherent complexities of regenerative medicine products, the advent of techniques such as gene editing and tissue organoids, and pragmatic problems of scaling-up cell manufacturing, conventional ways of thinking about and producing evidence are challenged. At the same time, the push to speed product approvals endures, but now in political and economic environments that include differing attitudes toward risk and patients’ roles in decision-making. The chapter highlights how crossing technological and political zones, data-driven approaches plus a return to observational data in particular are being incorporated into US regulatory law and product review.


Regenerative Medicine Products Technological Zones Learning Healthcare System Evidence Production Smart Regulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abraham, J. 2007. Drug Trials and Evidence Bases in International Regulatory Context. BioSocieties 2: 41–56.CrossRefGoogle Scholar
  2. Anderson, C. 2008. The End of Theory? The Data Deluge Makes the Scientific Method Obsolete. Wired, June 23.Google Scholar
  3. Angus, D. 2015. Fusing Randomized Trials with Big Data: The Key to Self-learning Health Care Systems? Journal of the American Medical Association 314 (8): 767–768.CrossRefGoogle Scholar
  4. Avorn, J., and A. Kesselheim. 2015. The 21st Century Cures Act—Will It Take Us Back in Time? New England Journal of Medicine 372 (26): 2473–2475.CrossRefGoogle Scholar
  5. Barazzetti, G., S.A. Hurst, and A. Mauron. 2016. Adapting Preclinical Benchmarks for First-in-human Trials of Human Embryonic Stem Cell-based Therapies. Stem Cells Translational Medicine 5 (8): 1058–1066. doi: 10.5966/sctm.2015-0222 CrossRefGoogle Scholar
  6. Barry, A. 2001. Political Machines: Governing a Technological Society. London: Athlone Press.Google Scholar
  7. Bateman-House, A., L. Kimberly, B. Redmond, N. Dubler, and A. Caplan. 2015. Right-to-try Laws: Hope, Hype, and Unintended Consequences. Annals of Internal Medicine 163 (10): 796–797.CrossRefGoogle Scholar
  8. Begley, C., and J. Ioannidis. 2015. Reproducibility in Science: Improving the Standard for Basic and Preclinical Research. Circulation Research 116 (1): 116–126.CrossRefGoogle Scholar
  9. Benson, K., and A.J. Hartz. 2000. A Comparison of Observational Studies and Randomized Controlled Trials. New England Journal of Medicine 342 (25): 1878–1886.CrossRefGoogle Scholar
  10. Bharadwaj, A. 2012. Enculturating Cells: The Anthropology, Substance, and Science of Stem Cells. Annual Review of Anthropology 41: 303–317.CrossRefGoogle Scholar
  11. Bharadwaj, A., and P. Glasner. 2009. Local Cells, Global Science: The Rise of Embryonic Stem Cell Research in India. London: Routledge.Google Scholar
  12. boyd, d., and K. Crawford. 2012. Critical Questions for Big Data. Information, Communication, & Society 15 (5): 662–679.Google Scholar
  13. Burnstein, D., and P. Burridge. 2014. Patient-Specific Pluripotent Stem Cells in Doxirubin Cardiotoxicity: A New Window into Personalized Medicine. Progress in Pediatric Cardiology 37 (1–2): 23–37.CrossRefGoogle Scholar
  14. Couzin, J., and G. Vogel. 2004. Renovating the Heart. Science 304 (5668): 184.CrossRefGoogle Scholar
  15. Dahabreh, I., and D. Kent. 2014. Can the Learning Healthcare System Be Educated with Observational Data? Journal of the American Medical Association 213 (2): 129–130.CrossRefGoogle Scholar
  16. Elkhenini, H., K. Davis, N. Stein, J. New, M. Delderfield, M. Gibson, J. Vestbo, A. Woodcock, and N. Bakerly. 2015. Using Electronic Medical Records (EMR) to Conduct Clinical Trials. BMC Medical Informatics and Decision Making 15: 8–18.CrossRefGoogle Scholar
  17. Epstein, S. 2007. Inclusion: The Politics of Difference in Medical Research. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  18. Etheredge, L. 2007. A Rapid-Learning Health System. Health Affairs 26 (2): 107–113.CrossRefGoogle Scholar
  19. Etheredge, L.M. 2014. Rapid Learning: A Breakthrough Agenda. Health Affairs 33 (7): 1155–1162.CrossRefGoogle Scholar
  20. Expert Advisory Committee on Regulation. 2004. Smart Regulation: A Regulatory Strategy for Canada. Report to the Government of Canada, Ottawa.Google Scholar
  21. Faulkner, A. 2015. Special Treatment? Exceptions and Exemptions in the Politics of Regenerative Medicine Gatekeeping in the UK in Global Context. Working Paper 46, Economic and Social Research Council.Google Scholar
  22. Food and Drug Administration. 2013. Paving the Way for Personalized Medicine: FDA’s Role in the New Era of Product Development. Report to the Department of Health and Human Services, Rockville, MD.Google Scholar
  23. Haddad, C., H. Chen, and H. Gottweis. 2013. Unruly Objects: Novel Innovation Paths and Their Regulatory Challenge. In The Global Dynamics of Regenerative Medicine: A Social Science Critique, ed. A. Webster, 88–117. London: Palgrave Macmillan.CrossRefGoogle Scholar
  24. Hey, T., S. Tansley, and K. Tolle. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Redmond, WA: Microsoft Research.Google Scholar
  25. Hoffman, S., and A. Podgurski. 2013. The Use and Misuse of Biomedical Data: Is Bigger Really Better? American Journal of Law & Medicine 39: 497–538.CrossRefGoogle Scholar
  26. Hogle, L.F. 2009. Pragmatic Objectivity and the Standardization of Engineered Tissues. Social Studies of Science 39 (5): 717–742.CrossRefGoogle Scholar
  27. ———. 2016a. The Ethics and Politics of Infrastructures: Creating the Conditions of Possibility for Big Data in Medicine. In The Ethics of Biomedical Big Data, ed. B. Mittelstadt and L. Floridi, 397–427. New York: Springer.CrossRefGoogle Scholar
  28. ———. 2016b. Data-intensive Resourcing in Healthcare. BioSocieties 11 (3): 372–393.CrossRefGoogle Scholar
  29. Hudis, C. 2015. Big Data: Are Large Prospective Randomized Trials Obsolete in the Future? Breast 24 (S1): S15–S18.CrossRefGoogle Scholar
  30. Institute of Medicine. 2007. The Learning Healthcare System. In The Workshop Summary of the Roundtable of Evidence-Based Medicine, ed. L. Olsen, D. Aisner, and J.M. McGinnis. Washington, DC: National Academies Press.Google Scholar
  31. ———. 2013. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. In Report from the Committee on the Learning Healthcare System, ed. M. Smith, R. Saunders, L. Stuckhardt, and J.M. McGinnis. Washington, DC: National Academies Press.Google Scholar
  32. International Society for Stem Cell Research. 2016. Guidelines for Stem Cell Research and Clinical Translation.
  33. Ioannidis, J. 2014. How to Make More Published Research True. PLoS Medicine 11 (10): e1001747. doi: 10.1371/journal.pmed.1001747 CrossRefGoogle Scholar
  34. Jasanoff, S. 2004. The Idiom of Co-production. In States of Knowledge: The Co-production of Science and the Social Order, ed. S. Jasanoff. New York: Routledge.CrossRefGoogle Scholar
  35. Kallinikos, J., and N. Tempini. 2014. Patient Data as Medical Facts: Social Media Practices as a Foundation for Medical Knowledge Creation. Information Systems Research 25 (4): 817–833.CrossRefGoogle Scholar
  36. Kitchin, R. 2014. Big Data, New Epistemologies, and Paradigm Shifts. Big Data and Society 1 (1): 1–12. doi: 10.1177/2053951714528481 CrossRefGoogle Scholar
  37. Klein, S., and M. Hostetter. 2013. In Focus: Learning Healthcare Organizations. Quality Matters. Online Newsletter of the Commonwealth Fund.
  38. Knaapen, L. 2014. Evidence-based Medicine or Cookbook Medicine? Addressing Concerns over the Standardization of Care. Sociology Compass 8 (6): 823–836.CrossRefGoogle Scholar
  39. Krumholz, H. 2014. Big Data and New Knowledge in Medicine: The Thinking, Training, and Tools Needed for a Learning Health System. Health Affairs 33 (7): 1163–1170.CrossRefGoogle Scholar
  40. Lambert, H. 2006. Accounting for EBM: Notions of Evidence in Medicine. Social Science & Medicine 62: 2633–2645.CrossRefGoogle Scholar
  41. Laustriat, D., J. Gide, and M. Peschanski. 2010. Human Pluripotent Stem Cells in Drug Discovery and Predictive Toxicology. Biochemical Society Transactions 38 (4): 1051–1057.CrossRefGoogle Scholar
  42. Leonelli, S. (2014) ‘What Difference Does Quantity Make? On the Epistemology of Big Data in Biology’. Big Data & Society 1(1): 1–11. doi: 10.1177/20539517145432395. Accessed 7 July 2015.CrossRefGoogle Scholar
  43. Löwy, I. 2003. Experimental Bodies. In Companion to Medicine in the Twentieth Century, ed. R. Cooter and J. Pickstone, 435–450. New York: Routledge.Google Scholar
  44. Marks, H. 2009. What Does Evidence Do? Histories of Therapeutic Research. In Harmonizing Drugs: Standards in the 20th-Century Pharmaceutical History, ed. C. Masutti Bonah, A. Rasmussen, and J. Simon, 81–100. Paris: Ed. Glyphe.Google Scholar
  45. Marks, P., C. Witten, and R. Califf. 2016. Clarifying Stem-cell Therapy’s Benefits and Risks. New England Journal of Medicine, November 30. doi: 10.1056/NEJMp1613723.
  46. Mayer-Schoenberger, V., and K. Cukier. 2013. Big Data: A Revolution that Will Transform How We Live, Think and Work. London: John Murray.Google Scholar
  47. Meystre, S., G. Savova, K. Kipper-Schuler, and J. Kurdle. 2008. Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research. Yearbook of Medical Informatics: 128–144.Google Scholar
  48. Montgomery, C. 2016. From Standardization to Adaptation: Clinical Trials and the Moral Economy of Anticipation. Science as Culture. Online in Advance of Publication. doi: 10.1080/09505431.2016.1255721.
  49. Mullard, A. 2015. Stem Cell Discovery Platforms Yield First Clinical Candidates. Nature Reviews Drug Discovery 14: 589–591.CrossRefGoogle Scholar
  50. Mykhalovskiy, E., and L. Weir. 2004. The Problem of Evidence-based Medicine: Directions for Social Science. Social Science and Medicine 59 (5): 1059–1069.CrossRefGoogle Scholar
  51. National Research Council Committee on a Framework for Developing a New Taxonomy of Disease. 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: National Academies Press.Google Scholar
  52. Olson, M. 2002. Pharmaceutical Policy Change and the Safety of New Drugs. Journal of Law and Economics 45 (2): 615–642.CrossRefGoogle Scholar
  53. Pearson, S. 2007. Standards of Evidence. In The Learning Healthcare System, Workshop Summary of the Roundtable on Evidence-Based Medicine, ed. L. Olsen, D. Aisner, and J.M. McGinnis, 171–183. Washington, DC: National Academies Press.Google Scholar
  54. Perriello, B. 2015. FDA, Med Tech in Bed on 21st Century Cures Act. Mass Device. Online Newsletter, December 21.
  55. Perrin, S. 2014. Preclinical Research: Make Mouse Studies Work. Nature News 507 (7493): 423–426.CrossRefGoogle Scholar
  56. Richardson, E. 2015. Health Policy Brief: Right-to-try Laws. Health Affairs. Online, March 5.
  57. Rosemann, A. 2014. Standardization as Situation-Specific Achievement: Regulatory Diversity and the Production of Value in Intercontinental Collaborations in Stem Cell Medicine. Social Science & Medicine 122: 72–80.CrossRefGoogle Scholar
  58. Rosemann, A., and N. Chaisinthop. 2016. The Pluralization of the International: Resistance and Alter-Standardization in Regenerative Stem Cell Medicine. Social Studies of Science 46 (1): 112–139. doi: 10.1016/j.socscimed.2014.10.018 CrossRefGoogle Scholar
  59. Roski, J., G.W. Bo-Linn, and T.A. Andrews. 2014. Creating Value in Health Care Through Big Data: Opportunities and Policy Implications. Health Affairs 33 (7): 1115–1122.CrossRefGoogle Scholar
  60. Schork, N.J. 2015. Personalized Medicine: Time for One-Person Trials. Nature 520: 609–611.CrossRefGoogle Scholar
  61. Tahir, D. 2015. Interest Groups Seek to Add Goodies to Fast-Moving FDA Overhaul Bill. Modern Healthcare, May 12.
  62. Timmermans, S., and M. Berg, eds. 2003. The Gold Standard: The Challenge of Evidence-based Medicine and Standardization in Health Care. Philadelphia: Temple University Press.Google Scholar
  63. Timmermans, S., and S. Epstein. 2010. A World of Standards But Not a Standard World: Toward a Sociology of Standards and Standardization. Annual Review of Sociology 36 (36): 69–89.CrossRefGoogle Scholar
  64. Timmermans, S., and A. Mauck. 2005. The Promises and Pitfalls of Evidence-based Medicine. Health Affairs 24 (1): 18–28.CrossRefGoogle Scholar
  65. Topol, E. 2011. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. New York: Basic Books.Google Scholar
  66. Tozzi, J. 2015. Safety Outsourced Under Bill Blessed by FDA, Medical Device Makers. Bloomberg News, December 21.
  67. Webster, A. 2013. Introduction: The Boundaries and Mobilities of Regenerative Medicine. In The Global Dynamics of Regenerative Medicine: A Social Science Critique, ed. A. Webster, 1–17. London: Palgrave Macmillan.CrossRefGoogle Scholar
  68. Weisz, G. 2005. From Clinical Counting to Evidence-based Medicine. In Body Counts: Medical Quantification in Historical and Sociological Perspectives, ed. G. Jorland, A. Opinel, and G. Weisz, 377–393. Montreal: McGill–Queen’s University Press.Google Scholar
  69. White House. 2015. A Strategy for American Innovation. Report of the National Economic Council and Office of Science and Technology Policy, October.
  70. Wills, C., and T. Moreiga, eds. 2010. Medical Proofs, Social Experiments: Clinical Trials in Changing Contexts. London: Ashgate.Google Scholar
  71. Wood, S., and D. Zuckerman. 2015. The 21st Century Cures Act Could Be a Harmful Step Backwards. Washington Post, November 19.
  72. Yamamoto, K., E. Sumi, T. Yamazaki, K. Asai, M. Yamori, S. Teramukai, et al. 2012. A Pragmatic Method for Electronic Medical Record-based Observational Studies: Developing an Electronic Medical Records Retrieval System for Clinical Research. British Medical Journal 2: e001622.Google Scholar
  73. Young, S., and A. Karr. 2011. Deming, Data, and Observational Research: A Process Out of Control and Needs Fixing. Significance: 116–120.Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Linda F. Hogle
    • 1
  1. 1.Department of Medical History & Bioethics, School of Medicine & Public HealthUniversity of Wisconsin–MadisonMadisonUSA

Personalised recommendations