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Value assessment in precision cancer medicine
Accepted Manuscript Title: Value Assessment in Precision Cancer Medicine Author: Adrian Towse Louis Garrison PII: DOI: Reference:
S2213-5383(16)30022-4 http://dx.doi.org/doi:10.1016/j.jcpo.2016.09.003 JCPO 74
To appear in: Received date: Accepted date:
1-5-2016 5-9-2016
Please cite this article as: Adrian Towse, Louis Garrison, Assessment in Precision Cancer Medicine, Journal of Cancer http://dx.doi.org/10.1016/j.jcpo.2016.09.003
Value Policy
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Value Assessment in Precision Cancer Medicine Authors Adrian Towse [email protected] (corresponding author) The Office of Health Economics Southside, 7th Floor, 105 Victoria Street, London SW1E 6QT United Kingdom Louis Garrison [email protected] Pharmaceutical Outcomes Research & Policy Program University of Washington School of Pharmacy 1959 NE Pacific St., H-375A Seattle, WA 98195 United States
Abstract Oncology is the initial prime clinical area of precision medicine applications. This paper identifies and discusses key conceptual, implementation, and policy issues in applying value assessment in precision cancer medicine. The economic complementarity of tests and diagnostics is important to recognize because of the challenges it creates in defining their specific contribution to value. There are three key aspects to this: (a) the institutional arrangments, (b) the evidence requirements, and (c) the division of value as between the drug and the diagnostic, such that both receive a value-related price. Controversy over the prices of some cancer medicines makes the possibility of targetting treatments to sub-populations attractive to payers. The value of test-drug combinations goes beyond health gain and health system cost-offsets to include several elements related to the value of knowing, such as the value of reduced uncertainty, of insurance, and of scientific spillovers. The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both is needed to encourge personalised medicine. However, application will become even more challenging in precision medicine given the increasing use of combination therapies and multiple biomarker-based tests. As assessment of the value of a drug-diagnostic combination cannot always be done at launch, payers and providers need diagnostic-specific value-assessment processes as well as drug value-assessment processes that are flexible over time. Evidence on the clinical utility of diagnostics is not easy to generate but is essential. Coverage with evidence development provides one route to obtain this. Highlights Oncology is the initial prime clinical area of precision medicine applications. Precision medicine in oncology faces new challenges given recent scientific progress in terms of combining new biomarker-based tests with new medicines
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The complementary of tests and diagnostics creates special challenges in terms of assigning the value contribution of each. The value of test-drug combinations goes beyond health gain and health system costoffets to include several elements related to the value of knowing, such as the value of reduced uncertainty. The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both is needed to encourge precision medicine. Evidence on the clinical utility of diagnostics is not easy to generate but is essential.
Introduction In this paper we discuss what is meant by precision medicine and why this has become an important topic in cancer treatment. We then consider how to assess value in precison medicine, focussing on the challenges of valuing the contributions of precison medicine diagnostic tests. We conclude with recommendations for policy makers as to how value assessment can be improved. Precision Medicine and Cancer Treatment The term ”precision medicine” has emerged in recent years as a refinement of the concept of personalized medicine or stratified medicine which have been used synonymously [1].The first most visible use was in a US National Research Council report [2]. It followed an earlier definition [3] and stated: ”Precision medicine refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or devices that are unique to the patient but rather to classify patients into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases that may develop, or in their response to a specific treatment. . . . Although the term ”personalized medicine” is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual.” This concept and term has been restated more recently by a working group under President Obama’s Personalized Medicine Initiative. This group gave this definition: “Precision medicine is an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment, and lifestyle. Precision medicine seeks to redefine our understanding of disease onset and progression, treatment response, and health outcomes through the more precise measurement of molecular, environmental, and behavioral factors that contribute to health and disease. This understanding will lead to more accurate
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diagnoses, more rational disease prevention strategies, better treatment selection, and the development of novel therapies.” Oncology is the area when the value assessment of precision medicine is first becoming an issue. This is for several reasons: 1. Cancer is the first disease area outside of single gene disorder diseases such as cystic fibrosis where researchers have found numerous clear links between the genetic characteristics of the disease (tumours in the case of cancer) and the responsiveness to treatments. In the case of single gene disorder diseases, most have very small patient populations and qualify as orphan drugs, which are exempted from many value assessment regimes; 2. Molecular diagnostic tests are initially being used primarily in oncology. Towse et al. [4] examined nine case studies to explore the economic value of molecular diagnostic tests. Five out of nine case studies were for oncology. 3. The disease burden of cancer in high income countries means that cancer is attracting substantial biopharmaceutical research and development (R&D) investment. Thus, the Personalised Medicine Coalition [5] reported that oncology drugs were the largest group of drug approvals in 2015 with 14 out of 44. It also reported that 35% of the novel new oncology drugs approved by the FDA in 2015 are personalised medicines as compared to 25% of the non-cancer approvals (using a definition of personalised medicine as drugs ”for which the label includes reference to specific biological markers, identified by diagnostic tools, that help guide decisions and/or procedures for their use in individual patients”). 4. Early access to medicines regulations, such as the FDA Breakthrough Designation (BTD) mean that these cancer medicines are coming to market based on intermediate biomarkers and surrogate endpoints such as progression-free survival (PFS). Of the 21 drugs approved with BTD in 2015, 10 were in oncology. Overall, 38 drugs have been given BTD approval of which 20 are oncology drugs [6]. The approval of oncology drugs through early access means that evidence of value may be required after launch. Whilst use of biomarkers and PFS may reduce development times and costs, clinicians and payers will be looking for evidence of the value of patient benefit when the drugs are used in clinical practice; 5. Controversy over the prices of some cancer medicines makes the possibility of targetting treatments to sub-populations attractive to payers. We would note, however that if we have a model of value driven primarily by health gain, i.e. payers are paying for health gain in the treated population, then if health gain is the same, but is achieved by treating a smaller patient group, i.e. excluding those who were not benefiting, then the revenues for the manufacturer, and so drug expenditure for the payers, will still be the same. Prices will rise as volumes fall [7].
The Economics of Using Drugs with Diagnostics Diagnostic tests may determine the risk of developing a disease, the presence of a disease, an individual’s prognosis or their treatment response. In general, we can think of diagnostics as being complementary technologies to other health care interventions [8], and as notably supporting the services of a health care professional or the targetting of a drug or other
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intervention. Specific drug-diagnostic combinations have also been called ”co-dependent” technologies reflecting one or both of (i) a regulatory requirement for the test to be used to determine whether the drug is applicable and (ii) the economic reality that using either technology separately is of limited value. The term companion diagnostic is also used in this context. The essence of complementarity or co-dependence is the need to assess the combined value of using the two interventions together. In this paper we review: 1. The elements of value that can matter to patients, clinicians, and payers. 2. The challenges of assessing value. There are three key aspects to this: (a) the institutional arrangments, (b) the evidence requirements, and (c) the division of value as between the drug and the diagnostic, such that both receive a value-related price. 3. The potential impact of future developments, with tests serving more than one drug and the availablility of next generation sequencing (NGS). It is particularly important in this context to understand the difference in the regulatory and economic environment for drugs and for diagnostics. Drug research is built upon three pillars: intellectual property (IP) protection, efficacy evidence requirements for licensing, and reimbursement prices that reflect value. Evidence on drug effectiveness and value is generated by the drug developer in order to obtain regulatory and pricing approval. The drug company generally has a patent (no one can copy the product) and also regulatory exclusivity on the data they generate (no one can reference the data to get regulatory approval by claiming that their product does the same). These can provide the market power (assuming there is evidence of additional health gain) that enables the company to negotiate a price that reflects value from the payer. Arguably all three of these are missing in diagnostics: (1) patents are easier to work around, (2) regulatory hurdles are lower, making it more difficult to prevent others appropriating the benefits of evidence from studies the diagnostic manufacturer has paid for; and (3) pricing is often cost-based rather than value-based. As a consequence, there may be underinvestment in both diagnostic innovation and in supporting evidence generation. Incentives may only aligned be when the drug company needs a diagnostic test to complement its drug, and is willing to make the investment in both to capture all of gains. Data exclusivity could be given to evidence for diagnostics. This would require “follow on” tests to replicate the evidence generated by the “first-in-class“ test (as is the case for biologics, for example). However, under current diagnostic regulations in the US and EU countries, laboratory developed tests (LDTs)—and in the EU any tests provided by public health providers—have to meet lower regulatory hurdles and thus could not be prevented from piggybacking on clinical utility evidence. In addition to the data exclusivity requirement, there may therefore need to be an expectation that payers will pay more for the test with the stronger evidence base (i.e., that they will ignore tests without an evidence base when making HTA assessments.) Of course a balance has to be struck, as is done with patenting. The objective is not to delay competition to provide innovative tests rather to ensure initial test developers have the potential to earn a return if they have evidence to support claims of value. Economic research is clearly needed on both effective incentives to prevent piggybacking for a least a period of time, and how long they should be put in place. Defining Value in Precision Medicine
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There are some important differences across health systems as to how they approach the question of assessing the value of new medical technologies, but, in general, the core element that all systems consider is the health gain–measured as combination of length of life and quality-of-life—and any cost-savings for the health system (often called ”cost-offsets”) [9]. Some national health systems explicitly consider some other factors such as any impact on productivity, the severity of the disease, proximity to death, and equity issues relating to the patient group involved (children are typically given priority). Adapting the framework in our earlier work [10] , we can illustrate how the use of precision medicine (via the use of molecular diagnostics) can generate value in oncology. There are three main pathways: A.
B.
C.
Reducing or avoiding the adverse effects associated with treatment (including the medical and nonmedical costs of managing them). Testing can (i) allow a treatment to receive marketing authorisation by improving the benefit-risk ratio associated with the treatment; or (ii) increase adoption of the treatment, in cases where a treatment is licensed but is not widely used because of its perceived unfavourable average benefit-risk balance when considered across a wide patient population. Reducing or avoiding time delays in selecting the most appropriate intervention. This has three main consequences: i. it can generate health gain. When a disease is at an advanced stage (e.g. metastatic cancer), identifying non-responders and switching them to an alternative dosage, treatment or care at the right time may have significant impact on patients’ length and/or quality of life; ii. it can generates cost savings as it can avoid or reduce the cost of treating nonresponders, including the cost of the drug; iii. it can avoid or reduces inconvenience to patients who do not have to experience a long diagnostic process or have to try different therapies to identify the most suitable one. Enabling a treatment effective only in a small fraction of the population to be made available or more widely available. This could happen by: i.
ii.
iii.
“Rescuing” treatments that may otherwise either not have been licensed or withdrawn because of the limited treatment effect across the overall population (favourable clinical effects in a subgroup are overwhelmed by the large group of non-responders). An example here is the EGFR mutation in NSCLC; Increasing the chance of a treatment meeting reimbursement criteria (if a diagnostic targeting responders improves cost-effectiveness), or being included in clinical guidelines (if evidence provided is deemed sufficient to change treatment protocols). Testing examples include HER2/neu in breast cancer and the KRAS mutation in treatments for colorectal cancer; Accelerating the R&D process for treatments when a biomarker or other genetic characteristics allowing for patient stratification is ascertained at an early stage of treatment development. An example here is the use of ALK test in NSCLC.
Garau et al. [10] also identify a benefit from reducing uncertainty about the value of potential new treatments and likely effectiveness of available treatments. A related type of uncertainty is
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the perceived value of information to patients of reduced uncertainty as to their medical condition independent of the expected health outcomes. The literature has defined it as the “value of knowing” or, as Ach et al. [11] put it, “knowing for the sake of knowing”, even if, in the extreme, the condition under examination is untreatable. HTA reviews of drugs and their companion diagnostics have generally not explicitly tried to capture the value of any psychological gains due to the reductions in uncertainty; however, the idea of the”value of knowing” has been recognized for a long time. We have recently attempted to expand this notion and identify some nuances among different aspects of the value of knowing [8]. They are not all to do with psychological gains, and we briefly delineate what we mean by each of five factors related to the value of knowing: 1. Reduction in uncertainty [12] reflecting the idea that a companion diagnostic, by increasing the certainty of a patient’s response to a medicine—would be more valuable to individual patients and hence they (or their payer) would be willing to pay more for the combination. Furthermore, as noted above, at the population level, greater certainty could lead to greater uptake and improved compliance. 2. The value of hope [13] is the notion that in some circumstances individuals become riskseekers in the sense that they would be willing to pay more for access to a technology with a long tail indicating that some patients have a much longer survival time than current therapy, even though the average life expectancy may be no greater, or even less, than standard therapy. 3. Real option value [14] for which the best example is that if a treatment can extend life, this opens up possibilities for individuals to benefit from future advances in medicine. Hence, they (or their payer) should be willing to pay more than simply the amount they would pay for a gain the life expectancy alone, as calculated under conventional methods because it provides the option of benefiting from further treatments 4. Insurance value [16] is related to the idea that insurance to cover innovations provides peace of mind, not just by protecting against catastrophic financial loss but also by protecting from catastrophic health loss. The focus is usually only on financial protection, in the form on an Extended Cost-effectiveness Analysis [15]. Lakdawalla et al. point out that greater value comes from the reassurance value of knowing of the existence of a treatment, or even of incentives to develop such a treatment. 5. Scientific spillovers arise because the benefit of scientific advances cannot be entirely appropriated by those making them. Improving knowledge creates opportunities for additional innovation by other. For example, proving that a particular agent works on a hypothesized pathway in a particular cancer means that the general understanding of that cancer is enhanced and thus further research can explore other pathways in the same cancer. This creates a commons problem with potential underinvestment, implying that patients may wish to reward developers with higher prices to encourage knowledge generation. Of course, one could argue that individual patients or payers would not or should not be willing to pay for these elements of ”knowing”. The debate about high cancer drug prices could be
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taken to imply that many think prices already substantially exceed the value of health gain, and thus no further premiums are appropriate for other elements of value. Our point is that in the context of precision medicine and the use of diagnostics, many of these other informationrelated factors beyond immediate health gain could come into play and should be recognized alongside appropriate recognition of health gain and other relevant economic effects. This does not, however, directly answer the question of how value should be assessed, evidenced, and attributed as between a drug and a diagnostic given that they are complementary, and how to meet the challenges of generating supporting evidence.
Value-Based Pricing for Diagnostics Molecular tests can determine the risk of developing a disease, the presence of a disease, an individual’s prognosis, or their treatment response. In this context we focus on ”predictive” tests that identify treatment response. Inflexible cost-based payment systems for diagnostics may be a significant barrier to the development to personalized medicine companion diagnostics. Historically, pricing and reimbursement systems for diagnostics have been focused on the expected cost of making and conducting the test (which may depend on the technology platform used) and not the value delivered. This has meant that the price of a new diagnostic is often based on the price of existing tests (“cross-walking”) with similar clinical use or with similar characteristics or based on production cost based on analytic steps. Oncotype Dx is an example of test that was able to work around this system in the US and obtain a putative valuebased price on the order of $3500 per test. The major economic benefit is that it avoids chemotherapy costs and side effects (including the risk of death) in women with early stage breast cancer by identifying women with very low risk of cancer recurrence. The manufacturer was able to circumvent usual coding and pricing practices in obtaining Medicare reimbursement in the US. The alternative would have to use “code stacking” of analytic steps, such as: RNA extraction, reverse transcription, gene amplification, and interpretation and report. But this would have resulting in a payment level of only about $540 [17]. In England, Oncotype Dx was recommended for use by NICE based on a calculation that took account of the QALY health gains from avoiding the adverse effects of chemotherapy, as well as the savings from reduced treatment cost. Garau et al [10] set out possible institutional mechanisms to deal with HTA for drugs and for diagnostics, making a case for two types of institutional arrangement: (1) a joint drug-diagnostic review (logically by the drug committee) of “at launch” technologies that combine a drug with a companion diagnostic test and (2) a separate diagnostics committee to assess new diagnostics that are not tied to a drug at launch using diagnostics-specific expertise. Given the low evidence requirements and IP protection levels in the US and EU regulatory environments, a pragmatic approach needs to be taken to collecting evidence on the clinical utility of diagnostics. For example, it could make sense to use small randomized studies (if not built into Phase 3 of drug development) that could lead to conditional reimbursement approval, but accompanied by the collection of post-launch real-world data.
Dividing value between between complementary tests and medicines
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Garrison and Austin [18] point out that as the diagnostic test and the associated treatment are “economic complements”, synergistically contributing to the total value created, it is essentially arbitrary—in a static situation—to apportion the value between them. Garrison and Towse [19] offer a proposal for dividing that value from a dynamic perspective, rewarding the treatment for the mechanism of action and the test for separating the better responders from the worse responders. It should be noted that this proposal relates primarily to predictive markers and companion diagnostics (and is less relevant for prognostic ones) though more subtle and complex aspects of economic complementary may come into play, e.g., in the case of NGS. Estimates of the average development cost of a new drug range between $1.5 billion to $2.5billion. In contrast, the cost to develop a new biomarker-based assay is on the order of $10 million to $50 million—at the high end if additional clinical trials are needed to validate the biomarker-based test. Consider a hypothetical case where a drug is earning $1 billion in annual revenues despite having only a 50% response rate. A reliable biomarker-based test is invented that can predict the responders. In theory, the payer would pay up to $500 million for the test, and perhaps a premium for the value of knowing (i.e., reduced uncertainty for both the payer and the patient. One could argue, however, that it was the drug manufacturer’s invention that created the health gain in the responders and, as the overall health gain is the same, but much larger per treated patient, the price could rise such that the drug still earns $1billion per annum. The extra value created by the test consists of (1) avoiding any adverse event treatment costs and related health losses in the non-responders plus (2) the “value-of-knowing” premium. (Note that if the premium were, say, 5% of total health gain, it would be worth $50 million: an amount that could support substantial evidence generation). With flexible value-based pricing, this logic would argue the innovator’s drug price would be roughly doubled (i.e., to $1 billion in revenues), and that the test innovator would receive a reward in relation to cost-savings and QALY gains in the non-responders plus a premium for the value of knowing (i.e., $50 million). This split is arbitrary in a static sense, but it can be argued that it reinforces dynamic efficiency by considering the relative size of the investments needed to develop a drug versus a diagnostic. Clearly, this issue deserves further research.
Multiple indications for drugs and multiple marker NGS tests In oncology, the historical linkage between specific mutations and specific cancers is evolving into more complex relationships with multiple biomarkers that may be predictive in multiple cancers. Since particular medicines, such as trastuzumab (Herceptin, Genentech) for HER2positive breast cancer, have been tied to a specific biomarkers that can be found in several different cancers: the use of the same biomarker and the same medicine in other cancers creates challenges for many health systems ”one time, one size fits all” reimbursement systems currently used to reward innovation in medicine. Health economists have long recognized that the cost-effectiveness of a given intervention is likely to vary among sub groups of patients, and that cost-effectiveness of medicines will vary by indication if they are used for multiple clinical conditions. For example, the cost-effectiveness of trasutuzumab in HER2-positive breast cancer varies dramatically between patients treated for metastatic breast cancer with an ICER on the order of $86,000 per QALY gained versus an ICER in early breast cancer of about $26,000 per QALY gained (Garrison and Veenstra, 2009). If valuebased reimbursement aims to reward innovation based on outcomes, then it is obvious that we should should pay different amounts for different indications; [20]. Experience to date suggests
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that this works when the products are differentiated, with different brand names, although in the case of bevacizumab (Avastin) and ranibizumab (Lucentis) in wet AMD , fixed vial-pricing created problems. In the case of oncology medicines, separate branding generally does not make sense. Different patient subgroups within an indication or patients with different indications will have different value. However, tablets or vials of medicines used in multiple indications must generally be priced the same given the ability of wholesalers, pharmacists, and clinicians to arbitrage across indications. Workshops exploring this issue in the UK and the USA have identified one obvious ”solution” to this problem, which would be to provide differential discounts across indications from the same list price. However, health systems have not routinely collected the needed data on indication, though it would certainly seem to be feasible. The risk-sharing registries operating in Italy and the Systemic Anti-Cancer Therapy (SACT) Dataset in the UK are examples of information collection that could be used to support multiindication pricing for oncology drugs. An analogous issue to the division of value in the drug-diagnostic case arises then when two or more medicines are used in combination as economic complements and the combination produces synergies that lead to greater value. This happens on many occasions in oncology. How is that reward to be divided? To take the HER2-positive breast cancer example a bit further, more recently, Genentech has developed pertuzumab (Perjeta) raising another layer of issues beyond indication-specific pricing. First, pertuzumab was developed under the hypothesis of a synergy of pathways in HER2-positive breast cancer. When the combination was used in metastatic breast cancer there were additional substantial gains in survival compared to just trastuzumab (and docetaxel): median progression-free survival was 18.5 months vs. 12.4 months in the control group)[21]. Hence, since both products will be taken longer than when trastuzumab is given alone because the drugs are generally taken until progession. Hence, the total drug cost of the treatment will increase not only because of the addition of pertuzumab but also because of the longer use of trastuzumab. In a health system that aims to reward value creation based on the additional QALYs gained, how should this reward be divided between the two? Pragmatically, trastuzumab already has a price per vial and the manufacturer will gain revenues based on that additional use. And pertuzumab would then be rewarded for the additional incremental value. If it were biologically possible that pertuzumab could have been invented first, then the split would be different. From a reward perspective, however, it would be more appropriate to reward pertuzumab with soemthing closer to all of the incremental gain. But without indication-specific pricing and some other flexibilities, this is not feasible under our current pricing practices. This is not so much of a problem as long as a single company owns both medicines, as Genentech does in this instance. However, one could easily imagine a circumstance where another company discovers the second, complementary medicine and would receive a smaller reward as a result of current pricing inflexibilities and hence their incentive to innnovate would be less. Indeed, one could argue that Genentech has the greater incentive to a find a second medicine to be used in combination, as they can appropriate all of the rewards. This also raises the question of the reward to the complementary test. In this instance, two alternative laboratory-based tests can be used—and immunohistochemistry (IHC), and a fluorescence in situ hybridization (FISH) test. Both have been priced on a cost basis (IHC at about $134 and FISH about $245): thus, they are capturing a minority of the total value created [12].
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Consider the case of next generation sequencing (NGS). NGS is often seen as a ”disruptive” innovation because of the significant decreases in cost of analysing many gene sequences in parallel substituting for a range of “traditional” tests. Thus, we can move away from a pairing of one test-one drug, or even one test per biomarker for multiple drug options. The price of obtaining information on individual-specific DNA profile has fallen dramatically and will continue to fall. But how valuable is this information? It is well-known that single genes or mutations are only rarely highly prognostic, for example, in the case of Huntington’s disease, and are rarely highly predictive of response to medicines, for example, in the case of some drug metabolizing enzymes. In general, chronic diseases are a complex result of multiple genes interacting with ennvironment, and responses to treatments are also complex. The critical issue is therefore the cost of generating an evidence base to show the clinical utility of a test and the need to convey the information to the patient and make treatment choices. This element presents the greatest challenge, and the costs of evidence generation are not declining [22] Overall, therefore the impact of NGS is not straightforward. There will be reduced assay test costs, and increased speed and accuracy. NGS replaces a suite of tests – but we may not have needed them all. There will be some reduction in time in identifying the correct drug regimen, and this can matter a lot. It may avoid a patient starting on the incorrect therapy whilst waiting for test results. Increased accuracy enables better patient stratification, fewer false positives and false negatives. But the overall health and cost implications are unclear. We might expect positive health gain but higher costs. In which applications will NGS be cost-effective?
Final Comments The efficient use of drugs and diagnostics in cancer treatment, including sending the correct incentives for innovation, faces a number of challenges. Practical steps to improve dynamic efficiency that can be taken include:
The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both the medicines and tests is needed to encourge personalised medicine. However, the application of it will be even more challenging in precision medicine given the use of combination therapies and multiple biomarkerbased tests; Assessment of the value of a drug-diagnostic combination cannot always be done at launch. Tests evolve; new drugs in the class appear. NGS presents new valueassessment challenges. Payers and providers need diagnostic-specific value-assessment processes as well as drug value-assessment processes that are flexible over time. Evidence on the clinical utility of diagnostics is not easy to generate but is essential. Flexibility is required in the short run, with use of ”coverage with evidence development.” In the medium term, regulatory reform is required.
References
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1. Academy of Medical Sciences. Realising the potential of stratified medicine. Report. London: The Academy of Medical Sciences, 2013. 2. National Research Council Committee on A Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease: National Academies Press (US); 2011. 3. President's Council of Advisors on Science and Technology. Priorities for Personalized Medicine,. US: Office of Science and Technology, 2008. 4. Towse A, Ossa D, Veenstra D, Carlson J, Garrison L. Understanding the economic value of molecular diagnostic tests: case studies and lessons learned. Journal of personalized medicine. 2013;3(4):288-305. 5. PMC. Personalized Medicine Coalition [Internet]. 2016 29/04/2016. Available from: http://www.personalizedmedicinecoalition.org/. 6. U.S. Food and Drug Administration. CDER Breakthrough Therapy Designation Approvals as of December 31 2015. U.S. Food and Drug Administation,, 2015. 7. Danzon P, Towse A. The economics of gene therapy and of pharmacogenetics. Value in Health. 2002;5(1):5-13. 8. Garrison Louis, Jorge M-F, Bernarda Z. The Value of Knowing and Knowing the Value: Improving the Health Technology Assessement of Complementary Diagnostics. EPEMED, 2016 2016 Fourthcoming. Report No. 9. Towse A, Barnsley P. APPROACHES TO IDENTIFYING, MEASURING, AND AGGREGATING ELEMENTS OF VALUE. International Journal of Technology Assessment in Health Care. 2013;29(04):360-4. 10. Garau M, Towse A, Garrison L, Housman L, Ossa D. Can and should valuebased pricing be applied to molecular diagnostics? Personalized Medicine. 2013;10(1):61-72. 11. Asch DA, Patton JP, Hershey JC. Knowing for the Sake of Knowing The Value of Prognostic Information. Medical Decision Making. 1990;10(1):47-57. 12. Garrison LP, Babigumira JB, Masaquel A, Wang BC, Lalla D, Brammer M. The Lifetime Economic Burden of Inaccurate HER2 Testing: Estimating the Costs of False-Positive and False-Negative HER2 Test Results in US Patients with Early-Stage Breast Cancer. Value in Health. 2015;18(4):541-6. 13. Lakdawalla DN, Romley JA, Sanchez Y, Maclean JR, Penrod JR, Philipson T. How cancer patients value hope and the implications for cost-effectiveness assessments of high-cost cancer therapies. Health Affairs. 2012;31(4):676-82. 14. Cook JP, Golec JH, Vernon JA, Pink GH. Real option value and path dependence in oncology innovation. International Journal of the Economics of Business. 2011;18(2):225-38. 15. Verguet S, Murphy S, Anderson B, Johansson KA, Glass R, Rheingans R. Public finance of rotavirus vaccination in India and Ethiopia: an extended costeffectiveness analysis. Vaccine. 2013;31(42):4902-10. 16. Lakdawalla D, Malani A, Reif J. The Insurance Value of Medical Innovation. National Bureau of Economic Research, 2015. 17. Gustavsen G, Phillips K, Pothier K. The reimbursement landscape for novel diagnostics: Current limitations, real world impact and proposed solutions. Weston, MA. 2010. 11
18. Garrison LP, Austin MF. The economics of personalized medicine: a model of incentives for value creation and capture. Drug information journal. 2007;41(4):501-9. 19. Garrison L, Towse A. Personalized Medicine: Pricing and Reimbursement Policies as a Potential Barrier to Development and Adoption, Economics of. Encyclopedia of Health Economics. 2014:484-90. 20. Bach PB. Indication-specific pricing for cancer drugs. JAMA. 2014;312(16):1629-30. 21. Baselga J, Cortés J, Im S-A, Clark E, Ross G, Kiermaier A, et al. Biomarker analyses in CLEOPATRA: A phase III, placebo-controlled study of pertuzumab in human epidermal growth factor receptor 2–positive, first-line metastatic breast cancer. Journal of Clinical Oncology. 2014;32(33):3753-61. 22. Deverka PA, Dreyfus JC. Clinical integration of next generation sequencing: coverage and reimbursement challenges. The Journal of Law, Medicine & Ethics. 2014;42(s1):22-41.
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S2213-5383(16)30022-4 http://dx.doi.org/doi:10.1016/j.jcpo.2016.09.003 JCPO 74
To appear in: Received date: Accepted date:
1-5-2016 5-9-2016
Please cite this article as: Adrian Towse, Louis Garrison, Assessment in Precision Cancer Medicine, Journal of Cancer http://dx.doi.org/10.1016/j.jcpo.2016.09.003
Value Policy
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Value Assessment in Precision Cancer Medicine Authors Adrian Towse [email protected] (corresponding author) The Office of Health Economics Southside, 7th Floor, 105 Victoria Street, London SW1E 6QT United Kingdom Louis Garrison [email protected] Pharmaceutical Outcomes Research & Policy Program University of Washington School of Pharmacy 1959 NE Pacific St., H-375A Seattle, WA 98195 United States
Abstract Oncology is the initial prime clinical area of precision medicine applications. This paper identifies and discusses key conceptual, implementation, and policy issues in applying value assessment in precision cancer medicine. The economic complementarity of tests and diagnostics is important to recognize because of the challenges it creates in defining their specific contribution to value. There are three key aspects to this: (a) the institutional arrangments, (b) the evidence requirements, and (c) the division of value as between the drug and the diagnostic, such that both receive a value-related price. Controversy over the prices of some cancer medicines makes the possibility of targetting treatments to sub-populations attractive to payers. The value of test-drug combinations goes beyond health gain and health system cost-offsets to include several elements related to the value of knowing, such as the value of reduced uncertainty, of insurance, and of scientific spillovers. The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both is needed to encourge personalised medicine. However, application will become even more challenging in precision medicine given the increasing use of combination therapies and multiple biomarker-based tests. As assessment of the value of a drug-diagnostic combination cannot always be done at launch, payers and providers need diagnostic-specific value-assessment processes as well as drug value-assessment processes that are flexible over time. Evidence on the clinical utility of diagnostics is not easy to generate but is essential. Coverage with evidence development provides one route to obtain this. Highlights Oncology is the initial prime clinical area of precision medicine applications. Precision medicine in oncology faces new challenges given recent scientific progress in terms of combining new biomarker-based tests with new medicines
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The complementary of tests and diagnostics creates special challenges in terms of assigning the value contribution of each. The value of test-drug combinations goes beyond health gain and health system costoffets to include several elements related to the value of knowing, such as the value of reduced uncertainty. The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both is needed to encourge precision medicine. Evidence on the clinical utility of diagnostics is not easy to generate but is essential.
Introduction In this paper we discuss what is meant by precision medicine and why this has become an important topic in cancer treatment. We then consider how to assess value in precison medicine, focussing on the challenges of valuing the contributions of precison medicine diagnostic tests. We conclude with recommendations for policy makers as to how value assessment can be improved. Precision Medicine and Cancer Treatment The term ”precision medicine” has emerged in recent years as a refinement of the concept of personalized medicine or stratified medicine which have been used synonymously [1].The first most visible use was in a US National Research Council report [2]. It followed an earlier definition [3] and stated: ”Precision medicine refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or devices that are unique to the patient but rather to classify patients into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases that may develop, or in their response to a specific treatment. . . . Although the term ”personalized medicine” is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual.” This concept and term has been restated more recently by a working group under President Obama’s Personalized Medicine Initiative. This group gave this definition: “Precision medicine is an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment, and lifestyle. Precision medicine seeks to redefine our understanding of disease onset and progression, treatment response, and health outcomes through the more precise measurement of molecular, environmental, and behavioral factors that contribute to health and disease. This understanding will lead to more accurate
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diagnoses, more rational disease prevention strategies, better treatment selection, and the development of novel therapies.” Oncology is the area when the value assessment of precision medicine is first becoming an issue. This is for several reasons: 1. Cancer is the first disease area outside of single gene disorder diseases such as cystic fibrosis where researchers have found numerous clear links between the genetic characteristics of the disease (tumours in the case of cancer) and the responsiveness to treatments. In the case of single gene disorder diseases, most have very small patient populations and qualify as orphan drugs, which are exempted from many value assessment regimes; 2. Molecular diagnostic tests are initially being used primarily in oncology. Towse et al. [4] examined nine case studies to explore the economic value of molecular diagnostic tests. Five out of nine case studies were for oncology. 3. The disease burden of cancer in high income countries means that cancer is attracting substantial biopharmaceutical research and development (R&D) investment. Thus, the Personalised Medicine Coalition [5] reported that oncology drugs were the largest group of drug approvals in 2015 with 14 out of 44. It also reported that 35% of the novel new oncology drugs approved by the FDA in 2015 are personalised medicines as compared to 25% of the non-cancer approvals (using a definition of personalised medicine as drugs ”for which the label includes reference to specific biological markers, identified by diagnostic tools, that help guide decisions and/or procedures for their use in individual patients”). 4. Early access to medicines regulations, such as the FDA Breakthrough Designation (BTD) mean that these cancer medicines are coming to market based on intermediate biomarkers and surrogate endpoints such as progression-free survival (PFS). Of the 21 drugs approved with BTD in 2015, 10 were in oncology. Overall, 38 drugs have been given BTD approval of which 20 are oncology drugs [6]. The approval of oncology drugs through early access means that evidence of value may be required after launch. Whilst use of biomarkers and PFS may reduce development times and costs, clinicians and payers will be looking for evidence of the value of patient benefit when the drugs are used in clinical practice; 5. Controversy over the prices of some cancer medicines makes the possibility of targetting treatments to sub-populations attractive to payers. We would note, however that if we have a model of value driven primarily by health gain, i.e. payers are paying for health gain in the treated population, then if health gain is the same, but is achieved by treating a smaller patient group, i.e. excluding those who were not benefiting, then the revenues for the manufacturer, and so drug expenditure for the payers, will still be the same. Prices will rise as volumes fall [7].
The Economics of Using Drugs with Diagnostics Diagnostic tests may determine the risk of developing a disease, the presence of a disease, an individual’s prognosis or their treatment response. In general, we can think of diagnostics as being complementary technologies to other health care interventions [8], and as notably supporting the services of a health care professional or the targetting of a drug or other
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intervention. Specific drug-diagnostic combinations have also been called ”co-dependent” technologies reflecting one or both of (i) a regulatory requirement for the test to be used to determine whether the drug is applicable and (ii) the economic reality that using either technology separately is of limited value. The term companion diagnostic is also used in this context. The essence of complementarity or co-dependence is the need to assess the combined value of using the two interventions together. In this paper we review: 1. The elements of value that can matter to patients, clinicians, and payers. 2. The challenges of assessing value. There are three key aspects to this: (a) the institutional arrangments, (b) the evidence requirements, and (c) the division of value as between the drug and the diagnostic, such that both receive a value-related price. 3. The potential impact of future developments, with tests serving more than one drug and the availablility of next generation sequencing (NGS). It is particularly important in this context to understand the difference in the regulatory and economic environment for drugs and for diagnostics. Drug research is built upon three pillars: intellectual property (IP) protection, efficacy evidence requirements for licensing, and reimbursement prices that reflect value. Evidence on drug effectiveness and value is generated by the drug developer in order to obtain regulatory and pricing approval. The drug company generally has a patent (no one can copy the product) and also regulatory exclusivity on the data they generate (no one can reference the data to get regulatory approval by claiming that their product does the same). These can provide the market power (assuming there is evidence of additional health gain) that enables the company to negotiate a price that reflects value from the payer. Arguably all three of these are missing in diagnostics: (1) patents are easier to work around, (2) regulatory hurdles are lower, making it more difficult to prevent others appropriating the benefits of evidence from studies the diagnostic manufacturer has paid for; and (3) pricing is often cost-based rather than value-based. As a consequence, there may be underinvestment in both diagnostic innovation and in supporting evidence generation. Incentives may only aligned be when the drug company needs a diagnostic test to complement its drug, and is willing to make the investment in both to capture all of gains. Data exclusivity could be given to evidence for diagnostics. This would require “follow on” tests to replicate the evidence generated by the “first-in-class“ test (as is the case for biologics, for example). However, under current diagnostic regulations in the US and EU countries, laboratory developed tests (LDTs)—and in the EU any tests provided by public health providers—have to meet lower regulatory hurdles and thus could not be prevented from piggybacking on clinical utility evidence. In addition to the data exclusivity requirement, there may therefore need to be an expectation that payers will pay more for the test with the stronger evidence base (i.e., that they will ignore tests without an evidence base when making HTA assessments.) Of course a balance has to be struck, as is done with patenting. The objective is not to delay competition to provide innovative tests rather to ensure initial test developers have the potential to earn a return if they have evidence to support claims of value. Economic research is clearly needed on both effective incentives to prevent piggybacking for a least a period of time, and how long they should be put in place. Defining Value in Precision Medicine
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There are some important differences across health systems as to how they approach the question of assessing the value of new medical technologies, but, in general, the core element that all systems consider is the health gain–measured as combination of length of life and quality-of-life—and any cost-savings for the health system (often called ”cost-offsets”) [9]. Some national health systems explicitly consider some other factors such as any impact on productivity, the severity of the disease, proximity to death, and equity issues relating to the patient group involved (children are typically given priority). Adapting the framework in our earlier work [10] , we can illustrate how the use of precision medicine (via the use of molecular diagnostics) can generate value in oncology. There are three main pathways: A.
B.
C.
Reducing or avoiding the adverse effects associated with treatment (including the medical and nonmedical costs of managing them). Testing can (i) allow a treatment to receive marketing authorisation by improving the benefit-risk ratio associated with the treatment; or (ii) increase adoption of the treatment, in cases where a treatment is licensed but is not widely used because of its perceived unfavourable average benefit-risk balance when considered across a wide patient population. Reducing or avoiding time delays in selecting the most appropriate intervention. This has three main consequences: i. it can generate health gain. When a disease is at an advanced stage (e.g. metastatic cancer), identifying non-responders and switching them to an alternative dosage, treatment or care at the right time may have significant impact on patients’ length and/or quality of life; ii. it can generates cost savings as it can avoid or reduce the cost of treating nonresponders, including the cost of the drug; iii. it can avoid or reduces inconvenience to patients who do not have to experience a long diagnostic process or have to try different therapies to identify the most suitable one. Enabling a treatment effective only in a small fraction of the population to be made available or more widely available. This could happen by: i.
ii.
iii.
“Rescuing” treatments that may otherwise either not have been licensed or withdrawn because of the limited treatment effect across the overall population (favourable clinical effects in a subgroup are overwhelmed by the large group of non-responders). An example here is the EGFR mutation in NSCLC; Increasing the chance of a treatment meeting reimbursement criteria (if a diagnostic targeting responders improves cost-effectiveness), or being included in clinical guidelines (if evidence provided is deemed sufficient to change treatment protocols). Testing examples include HER2/neu in breast cancer and the KRAS mutation in treatments for colorectal cancer; Accelerating the R&D process for treatments when a biomarker or other genetic characteristics allowing for patient stratification is ascertained at an early stage of treatment development. An example here is the use of ALK test in NSCLC.
Garau et al. [10] also identify a benefit from reducing uncertainty about the value of potential new treatments and likely effectiveness of available treatments. A related type of uncertainty is
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the perceived value of information to patients of reduced uncertainty as to their medical condition independent of the expected health outcomes. The literature has defined it as the “value of knowing” or, as Ach et al. [11] put it, “knowing for the sake of knowing”, even if, in the extreme, the condition under examination is untreatable. HTA reviews of drugs and their companion diagnostics have generally not explicitly tried to capture the value of any psychological gains due to the reductions in uncertainty; however, the idea of the”value of knowing” has been recognized for a long time. We have recently attempted to expand this notion and identify some nuances among different aspects of the value of knowing [8]. They are not all to do with psychological gains, and we briefly delineate what we mean by each of five factors related to the value of knowing: 1. Reduction in uncertainty [12] reflecting the idea that a companion diagnostic, by increasing the certainty of a patient’s response to a medicine—would be more valuable to individual patients and hence they (or their payer) would be willing to pay more for the combination. Furthermore, as noted above, at the population level, greater certainty could lead to greater uptake and improved compliance. 2. The value of hope [13] is the notion that in some circumstances individuals become riskseekers in the sense that they would be willing to pay more for access to a technology with a long tail indicating that some patients have a much longer survival time than current therapy, even though the average life expectancy may be no greater, or even less, than standard therapy. 3. Real option value [14] for which the best example is that if a treatment can extend life, this opens up possibilities for individuals to benefit from future advances in medicine. Hence, they (or their payer) should be willing to pay more than simply the amount they would pay for a gain the life expectancy alone, as calculated under conventional methods because it provides the option of benefiting from further treatments 4. Insurance value [16] is related to the idea that insurance to cover innovations provides peace of mind, not just by protecting against catastrophic financial loss but also by protecting from catastrophic health loss. The focus is usually only on financial protection, in the form on an Extended Cost-effectiveness Analysis [15]. Lakdawalla et al. point out that greater value comes from the reassurance value of knowing of the existence of a treatment, or even of incentives to develop such a treatment. 5. Scientific spillovers arise because the benefit of scientific advances cannot be entirely appropriated by those making them. Improving knowledge creates opportunities for additional innovation by other. For example, proving that a particular agent works on a hypothesized pathway in a particular cancer means that the general understanding of that cancer is enhanced and thus further research can explore other pathways in the same cancer. This creates a commons problem with potential underinvestment, implying that patients may wish to reward developers with higher prices to encourage knowledge generation. Of course, one could argue that individual patients or payers would not or should not be willing to pay for these elements of ”knowing”. The debate about high cancer drug prices could be
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taken to imply that many think prices already substantially exceed the value of health gain, and thus no further premiums are appropriate for other elements of value. Our point is that in the context of precision medicine and the use of diagnostics, many of these other informationrelated factors beyond immediate health gain could come into play and should be recognized alongside appropriate recognition of health gain and other relevant economic effects. This does not, however, directly answer the question of how value should be assessed, evidenced, and attributed as between a drug and a diagnostic given that they are complementary, and how to meet the challenges of generating supporting evidence.
Value-Based Pricing for Diagnostics Molecular tests can determine the risk of developing a disease, the presence of a disease, an individual’s prognosis, or their treatment response. In this context we focus on ”predictive” tests that identify treatment response. Inflexible cost-based payment systems for diagnostics may be a significant barrier to the development to personalized medicine companion diagnostics. Historically, pricing and reimbursement systems for diagnostics have been focused on the expected cost of making and conducting the test (which may depend on the technology platform used) and not the value delivered. This has meant that the price of a new diagnostic is often based on the price of existing tests (“cross-walking”) with similar clinical use or with similar characteristics or based on production cost based on analytic steps. Oncotype Dx is an example of test that was able to work around this system in the US and obtain a putative valuebased price on the order of $3500 per test. The major economic benefit is that it avoids chemotherapy costs and side effects (including the risk of death) in women with early stage breast cancer by identifying women with very low risk of cancer recurrence. The manufacturer was able to circumvent usual coding and pricing practices in obtaining Medicare reimbursement in the US. The alternative would have to use “code stacking” of analytic steps, such as: RNA extraction, reverse transcription, gene amplification, and interpretation and report. But this would have resulting in a payment level of only about $540 [17]. In England, Oncotype Dx was recommended for use by NICE based on a calculation that took account of the QALY health gains from avoiding the adverse effects of chemotherapy, as well as the savings from reduced treatment cost. Garau et al [10] set out possible institutional mechanisms to deal with HTA for drugs and for diagnostics, making a case for two types of institutional arrangement: (1) a joint drug-diagnostic review (logically by the drug committee) of “at launch” technologies that combine a drug with a companion diagnostic test and (2) a separate diagnostics committee to assess new diagnostics that are not tied to a drug at launch using diagnostics-specific expertise. Given the low evidence requirements and IP protection levels in the US and EU regulatory environments, a pragmatic approach needs to be taken to collecting evidence on the clinical utility of diagnostics. For example, it could make sense to use small randomized studies (if not built into Phase 3 of drug development) that could lead to conditional reimbursement approval, but accompanied by the collection of post-launch real-world data.
Dividing value between between complementary tests and medicines
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Garrison and Austin [18] point out that as the diagnostic test and the associated treatment are “economic complements”, synergistically contributing to the total value created, it is essentially arbitrary—in a static situation—to apportion the value between them. Garrison and Towse [19] offer a proposal for dividing that value from a dynamic perspective, rewarding the treatment for the mechanism of action and the test for separating the better responders from the worse responders. It should be noted that this proposal relates primarily to predictive markers and companion diagnostics (and is less relevant for prognostic ones) though more subtle and complex aspects of economic complementary may come into play, e.g., in the case of NGS. Estimates of the average development cost of a new drug range between $1.5 billion to $2.5billion. In contrast, the cost to develop a new biomarker-based assay is on the order of $10 million to $50 million—at the high end if additional clinical trials are needed to validate the biomarker-based test. Consider a hypothetical case where a drug is earning $1 billion in annual revenues despite having only a 50% response rate. A reliable biomarker-based test is invented that can predict the responders. In theory, the payer would pay up to $500 million for the test, and perhaps a premium for the value of knowing (i.e., reduced uncertainty for both the payer and the patient. One could argue, however, that it was the drug manufacturer’s invention that created the health gain in the responders and, as the overall health gain is the same, but much larger per treated patient, the price could rise such that the drug still earns $1billion per annum. The extra value created by the test consists of (1) avoiding any adverse event treatment costs and related health losses in the non-responders plus (2) the “value-of-knowing” premium. (Note that if the premium were, say, 5% of total health gain, it would be worth $50 million: an amount that could support substantial evidence generation). With flexible value-based pricing, this logic would argue the innovator’s drug price would be roughly doubled (i.e., to $1 billion in revenues), and that the test innovator would receive a reward in relation to cost-savings and QALY gains in the non-responders plus a premium for the value of knowing (i.e., $50 million). This split is arbitrary in a static sense, but it can be argued that it reinforces dynamic efficiency by considering the relative size of the investments needed to develop a drug versus a diagnostic. Clearly, this issue deserves further research.
Multiple indications for drugs and multiple marker NGS tests In oncology, the historical linkage between specific mutations and specific cancers is evolving into more complex relationships with multiple biomarkers that may be predictive in multiple cancers. Since particular medicines, such as trastuzumab (Herceptin, Genentech) for HER2positive breast cancer, have been tied to a specific biomarkers that can be found in several different cancers: the use of the same biomarker and the same medicine in other cancers creates challenges for many health systems ”one time, one size fits all” reimbursement systems currently used to reward innovation in medicine. Health economists have long recognized that the cost-effectiveness of a given intervention is likely to vary among sub groups of patients, and that cost-effectiveness of medicines will vary by indication if they are used for multiple clinical conditions. For example, the cost-effectiveness of trasutuzumab in HER2-positive breast cancer varies dramatically between patients treated for metastatic breast cancer with an ICER on the order of $86,000 per QALY gained versus an ICER in early breast cancer of about $26,000 per QALY gained (Garrison and Veenstra, 2009). If valuebased reimbursement aims to reward innovation based on outcomes, then it is obvious that we should should pay different amounts for different indications; [20]. Experience to date suggests
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that this works when the products are differentiated, with different brand names, although in the case of bevacizumab (Avastin) and ranibizumab (Lucentis) in wet AMD , fixed vial-pricing created problems. In the case of oncology medicines, separate branding generally does not make sense. Different patient subgroups within an indication or patients with different indications will have different value. However, tablets or vials of medicines used in multiple indications must generally be priced the same given the ability of wholesalers, pharmacists, and clinicians to arbitrage across indications. Workshops exploring this issue in the UK and the USA have identified one obvious ”solution” to this problem, which would be to provide differential discounts across indications from the same list price. However, health systems have not routinely collected the needed data on indication, though it would certainly seem to be feasible. The risk-sharing registries operating in Italy and the Systemic Anti-Cancer Therapy (SACT) Dataset in the UK are examples of information collection that could be used to support multiindication pricing for oncology drugs. An analogous issue to the division of value in the drug-diagnostic case arises then when two or more medicines are used in combination as economic complements and the combination produces synergies that lead to greater value. This happens on many occasions in oncology. How is that reward to be divided? To take the HER2-positive breast cancer example a bit further, more recently, Genentech has developed pertuzumab (Perjeta) raising another layer of issues beyond indication-specific pricing. First, pertuzumab was developed under the hypothesis of a synergy of pathways in HER2-positive breast cancer. When the combination was used in metastatic breast cancer there were additional substantial gains in survival compared to just trastuzumab (and docetaxel): median progression-free survival was 18.5 months vs. 12.4 months in the control group)[21]. Hence, since both products will be taken longer than when trastuzumab is given alone because the drugs are generally taken until progession. Hence, the total drug cost of the treatment will increase not only because of the addition of pertuzumab but also because of the longer use of trastuzumab. In a health system that aims to reward value creation based on the additional QALYs gained, how should this reward be divided between the two? Pragmatically, trastuzumab already has a price per vial and the manufacturer will gain revenues based on that additional use. And pertuzumab would then be rewarded for the additional incremental value. If it were biologically possible that pertuzumab could have been invented first, then the split would be different. From a reward perspective, however, it would be more appropriate to reward pertuzumab with soemthing closer to all of the incremental gain. But without indication-specific pricing and some other flexibilities, this is not feasible under our current pricing practices. This is not so much of a problem as long as a single company owns both medicines, as Genentech does in this instance. However, one could easily imagine a circumstance where another company discovers the second, complementary medicine and would receive a smaller reward as a result of current pricing inflexibilities and hence their incentive to innnovate would be less. Indeed, one could argue that Genentech has the greater incentive to a find a second medicine to be used in combination, as they can appropriate all of the rewards. This also raises the question of the reward to the complementary test. In this instance, two alternative laboratory-based tests can be used—and immunohistochemistry (IHC), and a fluorescence in situ hybridization (FISH) test. Both have been priced on a cost basis (IHC at about $134 and FISH about $245): thus, they are capturing a minority of the total value created [12].
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Consider the case of next generation sequencing (NGS). NGS is often seen as a ”disruptive” innovation because of the significant decreases in cost of analysing many gene sequences in parallel substituting for a range of “traditional” tests. Thus, we can move away from a pairing of one test-one drug, or even one test per biomarker for multiple drug options. The price of obtaining information on individual-specific DNA profile has fallen dramatically and will continue to fall. But how valuable is this information? It is well-known that single genes or mutations are only rarely highly prognostic, for example, in the case of Huntington’s disease, and are rarely highly predictive of response to medicines, for example, in the case of some drug metabolizing enzymes. In general, chronic diseases are a complex result of multiple genes interacting with ennvironment, and responses to treatments are also complex. The critical issue is therefore the cost of generating an evidence base to show the clinical utility of a test and the need to convey the information to the patient and make treatment choices. This element presents the greatest challenge, and the costs of evidence generation are not declining [22] Overall, therefore the impact of NGS is not straightforward. There will be reduced assay test costs, and increased speed and accuracy. NGS replaces a suite of tests – but we may not have needed them all. There will be some reduction in time in identifying the correct drug regimen, and this can matter a lot. It may avoid a patient starting on the incorrect therapy whilst waiting for test results. Increased accuracy enables better patient stratification, fewer false positives and false negatives. But the overall health and cost implications are unclear. We might expect positive health gain but higher costs. In which applications will NGS be cost-effective?
Final Comments The efficient use of drugs and diagnostics in cancer treatment, including sending the correct incentives for innovation, faces a number of challenges. Practical steps to improve dynamic efficiency that can be taken include:
The use of flexible value-based pricing for cancer drugs and for diagnostic tests based for incremental value for both the medicines and tests is needed to encourge personalised medicine. However, the application of it will be even more challenging in precision medicine given the use of combination therapies and multiple biomarkerbased tests; Assessment of the value of a drug-diagnostic combination cannot always be done at launch. Tests evolve; new drugs in the class appear. NGS presents new valueassessment challenges. Payers and providers need diagnostic-specific value-assessment processes as well as drug value-assessment processes that are flexible over time. Evidence on the clinical utility of diagnostics is not easy to generate but is essential. Flexibility is required in the short run, with use of ”coverage with evidence development.” In the medium term, regulatory reform is required.
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