Or Circulate Incentives , Intellectual Property , and Black Box Personalized Medicine

Abstract

Personalized medicine is reshaping the biomedical landscape. Where Big Data meets Big Health, it has been hailed as the next leap forward in health care, and is both a subject of health law and an object of innovation policy. Humans are inherently variable, and closely matching treatment to patients has the potential to save and extend lives by suggesting better treatments, to avoid unnecessary treatment, and to streamline the process of drug discovery and clinical trials—all important innovation goals. But the version of personalized medicine being implemented today is just an entrée into the realm of what huge amounts of data can tell us about our health and how to improve it. Current versions of personalized medicine rely on the simple relationships that we can explicitly identify and validate in clinical trials. But biology is complicated. This paper introduces into legal scholarship the concept of black box personalized medicine, which seeks to use more directly that biological complexity by finding and using more complex, implicit biological relationships within the troves of health data we are increasingly amassing. This new form of personalized medicine offers potentially immense benefits, but requires high investment in developing new data, models, and applications—all of which are hard to protect once they become public. The current set of intellectual property incentives, particularly after the Supreme Court’s recent decisions in Prometheus and Myriad, fails to provide the necessary incentives for that investment, and instead pushes firms toward simple diagnostics paired with devices or trade secrecy and proprietary data. This paper addresses the concepts underlying black box personalized medicine, explains why the current intellectual property landscape provides inadequate and misdirected incentives, and briefly suggests policy options to better align incentives. * Assistant Professor, University of New Hampshire School of Law. J.D., Columbia Law School, 2011. Ph.D. (Biological Sciences), Columbia Graduate School of Arts and Sciences, 2010. A portion of this work was completed while an Academic Fellow at Petrie-Flom Center for Health Law Policy, Biotechnology and Bioethics at Harvard Law School. I wish to thank Ana Bračič, Glenn Cohen, Matt Lawrence, Kevin Outterson, Geertrui Van Overwalle, Ben Roin, and Jeff Skopek for their helpful comments and feedback. This work benefited from feedback at the Health Law Professors’ Conference and the Munich Conference on Innovation and Competition. All errors are my own. 2 ROUGH DRAFT – DO NOT CITE OR CIRCULATE PRICE TABLE OF CONTENTS Abstract ................................................................................................................... 1 Table of Contents .................................................................................................... 2 Introduction ............................................................................................................. 2 I. A new conception of personalized medicine ....................................................... 7 A. Revolution in personalized medicine .......................................................... 8 1. What is personalized medicine? ........................................................ 8 2. Explicit personalized medicine .......................................................... 10 3. Black box personalized medicine ...................................................... 13 B. The benefits of black box personalized medicine ....................................... 18 1. Patient care ........................................................................................ 18 2. Drug discovery and development ...................................................... 18 II. Hurdles to development ...................................................................................... 20 A. Data gathering ............................................................................................. 20 1. Expense .............................................................................................. 21 2. HIPAA and other legal hurdles ......................................................... 22 B. Algorithm generation and validation ........................................................... 23 C. Validation .................................................................................................... 25 III. Failures of the current intellectual property regime .......................................... 27 A. Intellectual property before Mayo v. Prometheus ....................................... 29 B. Mayo v. Prometheus .................................................................................... 30 C. The impact of Prometheus on personalized medicine ................................. 33 1. Paired diagnostics .............................................................................. 34 2. Trade secrecy and proprietary data .................................................... 35 IV. Improving incentives ........................................................................................ 38 A. Incentives for datasets ................................................................................. 39 B. Incentives for algorithms ............................................................................. 41 1. Patents ................................................................................................ 42 2. Regulatory Exclusivity ...................................................................... 43 3. Prizes ................................................................................................. 46 C. Incentives for validation .............................................................................. 48 Conclusion .............................................................................................................. 49

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Cite this paper

@inproceedings{Brai2014OrCI, title={Or Circulate Incentives , Intellectual Property , and Black Box Personalized Medicine}, author={Ana Bra{\vc}i{\vc} and Glenn Cohen and Matt Lawrence and Kevin Outterson and Geertrui Van Overwalle}, year={2014} }