This conference is supported by University of Southern California
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.
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, we evaluate the extent, pace, and outcomes of the translation of new scientific knowledge into drug discovery efforts. Our 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.
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.
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.
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.
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, we consider the types of firms pursuing R&D in precision medicines, considering how PPM R&D activities have evolved over recent years.
In addition to the conference paper, the research was distributed as NBER Working Paper w24026, which may be a more recent version.
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.
Breast cancer patients typically receive either conventional beam radiation or intensity modulated radiation therapy (IMRT) following surgery. Medicare spending is $8,000 higher for patients who receive IMRT, but there is little evidence that IMRT is superior to conventional beam radiotherapy. Using SEER-Medicare data, Howard, Hockenberry, and David show that patients treated in freestanding radiation therapy centers, most of which are physician-owned, are substantially more likely to receive IMRT. Use of a readily-observable marker of patients’ ability to benefit from IMRT, tumor laterality, has a strong impact on its use. However, incentives affect how physicians incorporate the marker into treatment decisions: patients treated in freestanding centers are more likely to receive IMRT regardless of whether their tumor is in the left or right breast. 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.
In addition to the conference paper, the research was distributed as NBER Working Paper w24054, which may be a more recent version.
Personalized Medicine When Physicians Induce Demand
Orphan Drug Designations as Valuable Intangible Assets for IPO Investors in Pharma-Biotech Companies.
The Value of Pharmacogenomic Information
The Information Pharms Race and Competitive Dynamics of Precision Medicine: Insights from Game Theory
Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments