Economic Dimensions of Personalized and Precision Medicine

September 13-14, 2017
Ernst R. Berndt of MIT; Dana Goldman of University of Southern California; and John Rowe of Columbia University, Organizers

Ernst R. Berndt, and Mark Trusheim, MIT

The Information Pharms Race and Competitive Dynamics of Precision Medicine

Precision medicines inherently fragment treatment populations, generating small-population markets, creating high-priced "niche busters" rather than broadly prescribed "blockbusters". It is plausible to expect that small markets will attract limited entry in which a small number of interdependent differentiated product oligopolists will compete, each possessing market power. Multiple precision medicine market situations now resemble game theory constructs such as the prisoners' dilemma and Bertrand competition. The examples often involve drug developer choices created by setting the cut-off value for the companion diagnostics to define the precision medicine market niches and their payoffs. Precision medicine game situations may also involve payers and patients who attempt to change the game to their advantage or whose induced behaviors alter the payoffs for the developers. The variety of games may predictably array themselves across the lifecycle of each precision medicine indication niche and so may become linked into a sequentially evolving meta-game. Berndt and Trusheim hypothesize that certain precision medicine areas such as inflammatory diseases are becoming complex simultaneous multi-games in which distinct precision medicine niches compete. Those players that learn the most rapidly and apply those learnings the most asymmetrically will be advantaged in this ongoing information pharms race.

Manuel I. Hermosilla, Johns Hopkins University, and Jorge A. Lemus, University of Illinois at Urbana-Champaign

Therapeutic Translation in the Wake of the Genome

The completion of Human Genome Project in 2003 enabled the systematic study of gene-disease associations. Exploiting a rich dataset, and focusing on the first generation of studies on gene-disease associations, Hermosilla and Lemus evaluate the extent, pace, and outcomes of the translation of new scientific knowledge into drug discovery efforts. Their preliminary results support a "hype" hypothesis. Overly optimistic expectations about the potential of genetic-based methods for the identification of novel drug targets fueled initial fast-paced translation. The extent of translation gradually slowed down and ultimately vanished as lackluster clinical trial performance began to be observed. Some of the steps required to advance this project include: (i) to refine the causality framework by developing a finer linkage between studies and discovery attempts and instruments for market potential; (ii) to generate an external metric for technological hype from media articles and leveraging tools of sentiment analysis; (iii) to strengthen our analysis of clinical trial performance; and (iv) to assemble and incorporate into the analysis a data set of patent applications in the US.

Kristopher Hult, University of Chicago

Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments

Individuals respond to pharmaceutical treatments differently due to the heterogeneity of patient populations. This heterogeneity can make it difficult to determine how efficacious or burdensome a treatment is for an individual patient. Personalized medicine involves using patient characteristics, therapeutics, or diagnostic testing to understand how individual patients respond to a given treatment. Personalized medicine increases the health impact of existing treatments by improving the matching process between patients and treatments and by improving a patient's understanding of the risk of serious side effects. Hult compares the health impact of new treatment innovations with the potential health impact of personalized medicine. They find that the impact of personalized medicine depends on the number of treatments, the correlation between treatment effects, and the amount of noise in a patient's individual treatment effect signal. For multiple sclerosis treatments, Hult finds that personalized medicine has the potential to increase the health impact of existing treatments by roughly 50 percent by informing patients of their individual treatment effect and risk of serious side effects.

Rachel Lu, Chang Gung University, Taiwan; Karen Eggleston, Stanford University and NBER; and Joseph T. Chang, Chang Gung Memorial Hospital

Economic Dimensions of Personalized and Precision Medicine in Asia: Evidence from Breast Cancer Treatment in Taiwan

The high costs of precision medicine raise the concern that their clinical use will exacerbate income-related disparities in healthcare utilization and health outcomes, especially in resource-poor settings. Lu, Eggleston, and Chang study treatment of HER2-positive breast cancer in Taiwan between 2004 and 2015 as a case study of disparities associated with personalized medicine. Analyzing a unique dataset linking medical claims, cancer registry data and proxies for household and area-level income, the researchers find that lower-income patients are more likely to be diagnosed with later stages of cancer, and this pattern renders NHI coverage of target therapy pro-poor even before coverage of the diagnostic test. Moreover, the expansion of NHI coverage—including the FISH diagnostic test and target therapy for early-stage breast cancer—strengthened the pro-poor distribution of genetic testing and target treatment, albeit only marginally. The researchers' regression analyses also confirmed the hypothesis that conditional on having late stage or metastatic breast cancer and controlling for income, the proportion receiving target therapy decreases with geographic remoteness. Taiwan’s experience illustrates that personalized medicine can disproportionately benefit the poor even when introduced without coverage of the companion diagnostic test, although geographic and other disparities may persist.

Frank R. Lichtenberg, Columbia University and NBER, and Rebecca A. Pulk, Marc S. Williams, and Eric Wright, Geisinger Health System

The Social Cost of Suboptimal Medication Use and the Value of Pharmacogenomic Information: Evidence from Geisinger

Many drugs don't work the same way for everyone, but it is often difficult to predict who will benefit from a medication, who will not respond at all, and who will experience adverse drug reactions, which are a significant cause of hospitalizations and deaths in the United States. In this study Lichtenberg, Pulk, Williams, and Wright will use a unique database, consisting of a 10-year history of clinical and claims data on tens of thousands of patients linked to their genetic information obtained by whole exome sequencing, to estimate the social cost of suboptimal medication use and the value of pharmacogenomic information.

Amitabh Chandra, Harvard University and NBER; Craig Garthwaite, Northwestern University and NBER; and Ariel Dora Stern, Harvard University

Characterizing the Drug Development Pipeline for Precision Medicines

Precision medicines – therapies that rely on genetic, epigenetic, and protein biomarkers – create a better match between individuals with specific disease subtypes and medications that are more effective for those patients. These treatments are expected to be both more effective and more expensive than conventional therapies, implying that their introduction is likely to have a meaningful effect on health care spending. Using a comprehensive database of over 140,000 global clinical trials, Chandra, Garthwaite, and Stern describe the drug development pipeline for precision medicines by characterizing drug development efforts over the past two decades. They identify clinical trials for potential precision medicines (PPMs) as those that use one or more relevant biomarkers. The researchers then further segment trials based on the nature of the biomarker(s) used and other trial features with economic implications. Since cancers represent a set of diseases in which precision therapies are already successfully used, and since cancer applications of precision medicine are expected to grow rapidly over the coming years, the researchers separately characterize cancer PPMs. They also summarize the role of National Institutes of Health (NIH) in supporting the existing pipeline of precision medicines, by asking what share of pipeline precision medicines rely on research supported by NIH grants. Finally, Chandra, Garthwaite, and Stern consider the types of firms pursuing R&D in precision medicines, considering how PPM R&D activities have evolved over recent years.

Mark Pauly, University of Pennsylvania and NBER

Cost Sharing in Insurance Coverage for Precision Medicine

John A. Graves and Josh Peterson, Vanderbilt University

Rational Integration of Genomic Healthcare Technology: Evidence from PREDICT

David H. Howard, Emory University; Jason Hockenberry, Emory University and NBER; and Guy David, University of Pennsylvania and NBER

Personalized Medicine When Physicians Induce Demand

The impact of personalized medicine tests on health care spending will depend on how they affect treatment decisions. Howard, David, and Hockenberry show that when physicians face incentives to induce demand, the introduction of a test will increase overall treatment rates. They show that breast cancer patients treated in freestanding radiotherapy clinics, where physicians face stronger incentives to induce demand, are more likely to receive a costly, low value form of radiotherapy called intensity modulated radiation therapy (IMRT). Differences in the use of IMRT between patients more or less likely to benefit do not differ between freestanding and hospital-based clinics. These results highlight the challenge of maximizing the benefit of tests that imperfectly predict patients' ability to benefit from a treatment in an environment where physicians' compensation is tied to the volume of treatments they provide.

Cyril Benoit, Philippe Gorry, Diego Useche, and Martin Zumpe, University of Bordeaux

Empirical Economic Analysis of Orphan Drug Innovation

Worldwide, governments are trying to stimulate drug R&D for unmet health needs with public policies. A common policy between the United States (US) and European Union (EU) is the legislation on Orphan Drugs (OD) dedicated to treatments for rare diseases. Drug development for those diseases has been limited mainly by the prohibitive cost of investing in a novel drug with poor market potential. To encourage the development of such drugs, OD legislation was put in place in the United States in 1983 and in the European Union in 2000. The introduction of this status has brought regulatory and economic incentives including market exclusivity (ME), fee reductions, clinical trial reimbursement to encourage R&D in the pharmaceutical industry. In the US, ME is granted by the Food & Drug Administration (FDA) for 7 years upon MA, while in the EU, the European Medicine Agency (EMA) provide a 10 years' extension. The ME provision is stronger than a patent: it cannot be interrupted by a competitor even if the underlying patent has expired. With the award by FDA and EMA of OD MA to more than 500 therapeutic indications, this legislation has contributed to the launch of many new drugs.
However, the increased availability of OD with their very high cost, and their use for chronic disease raises a debate about accessibility, cost-effectiveness, and reimbursement by health protection systems. The concept of personalized medicine was developed two decades ago by pharmaceutical companies, and now became a daily reality in oncology with companion diagnostics. With pharmacogenomics profiling of tumors in patients, the possibilities offered by precision medicine today are immense. So, it becomes a major issue for health economics and the sustainability of health insurance systems as a potential source of future social inequality. While OD legislation has more than 30 years in US, and more than 15 years in EU, one may wonder about the effectiveness of this legislation on the development of new OD, on the profitability of these OD for the pharmaceutical industry, and the rationality of the pricing and reimbursement of these drugs. The expected results would pave the way for a better understanding of the economic dimensions of innovative drugs in precision medicine.
As most of the incentives are not contingent to an ultimate drug R&D success, one may wonder about the effects of such public policy. Case studies have supported the opinion that the OD legislation has played an important role in drug R&D for rare diseases, but little empirical research has been performed on the subject. No existing research has analyzed the link between the characteristics of OD market exclusivity or tax reimbursement, and these downstream effects. Scholars have proposed cost/effectiveness and value-based pricing as a mean to estimate a drug price that is linked to the benefits it offers to patients and society. However, one of the key components of debates on rising health care costs is the cost of R&D and the role of private sector investments.
Benoit, Gorry, Useche, and Zumpe's research program focuses on 4 main objectives: the effects of bureaucratic politics on OD market, the attractiveness of private investment in OD R&D, the stock market valuation of OD firms, and the impact of OD market extension provision. More precisely, they tried to answer to the following questions: (i) why the number of OD designations (as well as OD market approvals) applications approved by the FDA in US is still significantly higher than in it is the Europe by EMA; (ii) does the OD portfolio size prior to IPO influence positively or negatively the amount of cash collected by pharma-biotech firms going public? (iii) does OD market approval announcements have a financial impact on fund-raising capacities of the biotechnology firms on stock market? (iv) does the OD ME provision increases the effective market monopoly.
To answer those questions, first, researchers explored the regulatory and reputation power of FDA and EMA agencies on OD application salami-slicing firm' strategy. Second, they explored whether disclosure of information about a firm's innovativeness through OD designation prior to Initial Public Offering (IPO), can reduce problems of asymmetric information and risk, and help firms in attracting investors. Thirdly, they investigated the financial impact of the OD market approval announcement on firm market valuation, with an event-study methodology based on the efficient market hypothesis. Fourthly, OD market exclusivity was compared to patent monopoly for all OD launched on the market since 1983.
By measuring the time lag of OD designations and MA first decisions, the number of designation by molecule and OD market approval, the percentage of MA drugs with more than for one designation, between FDA and EMA, Benoit, Gorry, Useche, and Zumpe show that differences between US and EU market is linked to FDA regulatory power sustaining drug innovation while EMA reputation power is sanctioning salami-slicing strategy.
The signaling power of OD designations to attract private investments is positive and statistically significant. An additional OD application prior to an IPO increases the IPO proceeds by about 10% in the US stock markets. Researches attempted to take into account for endogeneity of OD designations prior to IPO by considering simultaneous relationship between the firm innovative outputs and IPO performance.
Moreover, the OD market approval decisions have also a significant influence on stock price movements and abnormal returns of the pharma-biotech companies.
Finally, preliminary results indicate that the OD ME provision increases the effective market monopoly of 58% OD designations, but only with a small gain of by 0.2 year on average. This small benefit of the OD ME is decreasing along the past decades with large variations observed among diseases and drug classes. It remains to explore if there is any difference according to the sponsor profile.
Those preliminary results clearly indicate that the OD status, constitutes an important stimulus for the investor despite the reputation and regulatory power of the drug agencies. They suggest that this legislation favors future OD research via improved fund-raising capacities of the biotechnology firms. However, it seems that the OD market exclusivity provision is not the most effective incentive. Therefore, it remains to address in the future to which extend the tax-credit incentivizes firms to develop OD.

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