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Economic Incentives for Evidence Generation: Promoting an Efficient Pathto Personalized Medicine

Adrian Towse, MA, MPhil1,�, Louis P. Garrison, Jr., PhD2

1Office of Health Economics, London, UK; 2University of Washington, Seattle, WA, USA

A B S T R A C T

The preceding articles in this volume have identified and discussed awide range of methodological and practical issues in the develop-ment of personalized medicine. This concluding article uses theresulting insights to identify implications for the economic incen-tives for evidence generation. It argues that promoting an efficientpath to personalized medicine is going to require appropriateincentives for evidence generation including: 1) a greater willingnesson the part of payers to accept prices that reflect value; 2) consid-eration of some form of intellectual property protection (e.g., dataexclusivity) for diagnostics to incentivize generation of evidence ofclinical utility; 3) realistic expectations around the standards forevidence; and 4) public investment in evidence collection to comple-ment the efforts of payers and manufacturers. It concludes that such

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ohe.org.ndence to: Adrian Towse, Office of Health Economic

incentives could build and maintain a balance among: 1) realisticthresholds for evidence and the need for payers to have confidencein the clinical utility of the drugs and tests they use; 2) payment forvalue, with prices that ensure cost-effectiveness for health systems;and 3) levels of intellectual property protection for evidence gener-ation that provide a return for those financing research and develop-ment, while encouraging competition to produce both better andmore efficient tests.Keywords: economic incentives, personalized medicine, pharma-cogenetics, stratified medicine.

Copyright & 2013, International Society for Pharmacoeconomics andOutcomes Research (ISPOR). Published by Elsevier Inc.

Introduction

The preceding articles in this volume have identified and dis-cussed a wide range of methodological and practical issues in thedevelopment of personalized medicine. As O’Donnell [1] pointsout in the introduction, they cover three broad topic areas:comparative effectiveness research, drug development, and eco-nomic evaluation. In this concluding article, we attempt—froman economic perspective—to identify the insights and impli-cations for the crosscutting theme of economic incentives forevidence generation.

Definitional Issues

We follow Redekop and Mladsi [2] in using the term personalizedmedicine (PM) in this article, while recognizing that this typicallyinvolves stratifying patients into subpopulations and that we arenot just using genetic information to “prevent, diagnose andtreat.” As O’Donnell notes, it is important to avoid “second-orderdiscourse” from distracting us from the “pressing issues at hand”[1]. As Redekop and Mladsi note, most economists would be quickto point out that all medical treatments should be personalized inthe sense that the physician (the agent) should advise the patient(the principal) taking into account patient preferences, theevidence base that supports the likely benefits and risks of

different treatment choices, and the cost to the payer and tothe patient. The important difference in PM is the use of abiomarker-based diagnostic test [3] to further define and identifya subgroup of patients for whom the treatment performs better—in terms of either cost-effectiveness or benefit-risk balance. Thus,we restrict our use of the term PM to refer to this biomarker-based stratification.

The Progress of PM

We share the view of O’Donnell that “there remains a generalsense of dissatisfaction about the progress of personalizedmedicine” [1]. It is not hard to understand how the excessiveoptimism—even hype—of a decade ago has led to some cynicalviews about the possibility of realizing the promise that PM holdsfor the future. In the year 2000, Francis Collins, the current headof the U.S. National Institutes of Health said, “In the next five toseven years, we should identify the genetic susceptibility factorsfor virtually all common diseases—cancer, diabetes, heart dis-ease, the major mental illnesses—on down that list” [4]. Clearly,this has not happened.

In 2005, a multidisciplinary exercise at the University ofWashington—including geneticists, physicians, pharmacists,and economists—reached a less sanguine view about the speed

ociety for Pharmacoeconomics and Outcomes Research (ISPOR).

o conflicts of interest with regard to the content of this article.s, Southside, 7th Floor, 105 Victoria Street, London SW1E 6QT, UK.

V A L U E I N H E A L T H 1 6 ( 2 0 1 3 ) S 3 9 – S 4 3S40

at which this promising future will unfold. In the “most likely”scenario for the year 2015, they predicted:

By 2015, approximately 10 years from now, a variety of testkits using various biological markers testing will be feasiblewhen rapid test results are needed. Yet, the discovery andvalidation of pharmacogenomic associations will likely con-tinue at a similar measured pace. A notable development willbe the identification of clinically useful pharmacogenomicassociations in drug development trials outside of oncology.We expect that 10 to 15 pharmacogenomic tests will be inroutine use in clinical practice. Although the majority willcontinue to be in oncology, evaluating both tumor and patientgenetics, several tests outside of oncology will be used byprimary care clinicians to guide treatment decisions. [5]

This prediction is arguably still on target. It is clear thatseveral genetic tests are widely used, mainly in oncology [6];however, the total number in routine clinical use remains fairlylimited. The articles here have mentioned a number of examplesoutside of oncology, including CYP2C19 testing for clopidogreland HLA-B*5701 testing for abacavir. In relation to drug develop-ment also, the picture is mixed. Trastuzumab (Herceptin, Roche)for HER2þ breast cancer has often been cited as the “poster child”of PM. Despite the HER2 target receptor being identified in themid-1980s, it was not until 1998 that it was first approved by theU.S. Food and Drug Administration for HER2þ metastatic breastcancer, and for HER2þ early breast cancer in 2006. Its impact isstill evolving, with evidence of a synergistic effect with pertuzu-mab, which acts on the HER3 receptor.

At the other end of the speed-of-development spectrum,however, two highly significant clinical improvements—cures ornear cures for some patients—were developed and adopted veryquickly: imatinib (Gleevec, Novartis) for Philadelphia chromosome-positive (Phþ) chronic myelogenous leukemia and crizotinib(Xalkori, Pfizer) for a small targeted group of non–small cell lungcancer patients with anaplastic lymphoma kinase (ALK)mutations.

The scientific challenges are therefore being met in somecases but clearly remain. They are accompanied by a related setof challenges in terms of evidence, economic evaluation, reim-bursement, and regulation. The articles in this volume havetouched on all of these. We would like to reiterate and highlightsome of these points, placing particular emphasis on how each ofthese challenges affects or is affected by economic incentives.

Economic Evaluation Challenges and the Value to a Patient ofKnowing

In general, the methods of economic evaluation in PM are nodifferent than standard cost-effectiveness analysis (CEA). Besideshealth gain and cost-offsets captured in the usual CEA for atypical patient, however, we can identify at least three otherways in which PM might create additional value: 1) reducing thepatient’s uncertainty about the likelihood of successful treatment—the “value of knowing”— and as a result; 2) improving adher-ence and thus health outcomes for treated patients; and 3)raising overall uptake and utilization at a population level [7].

Annemans et al. [8] very effectively explain, however, howadding a PM test or sequence of tests before the clinical treat-ment pathway begins creates some new measurement chal-lenges for traditional CEA. For example, where will we get thedata on the clinical and economic consequences for false-negative and false-positives? Could they even be identified inmost commonly used trial designs? It is hoped that theirnumbers will be small, yet this makes the measurement ofconsequences less accurate.

Annemans et al. also allude to the “value of knowing,” byplacing a “special emphasis on process utility.” Information isimportant. As they put it: “even if a test result will not lead tochanging treatment, the actual value of receiving the communi-cation about the results and the associated advice cannot beignored.” Payne and Annemans [9] note that there is evidence tosupport the “added value from information” for clinicians andpatients. Annemans et al. note that contingent valuationapproaches have been used to measure and value the benefit ofknowing. One example of this is Neumann et al. [10]. Interest-ingly, they suggest the relevance of “capability” theory andmeasurement as a research route to explore. They also pointout that “a testing strategy does not necessarily lead to moreQALYs.” In other words, the effect of testing might be to restrictuse to a subgroup, reducing the absolute quality-adjusted life-year gains from treatment. This is because although targetingmay be cost-effective, it may mean that some patients for whomthe drug would have been effective do not receive treatment. Thiscould be because some of the “nonresponders” would actuallyrespond to some degree, and in addition there may be some falsenegatives, patients misclassified as nonresponders by the test.Thus, efficiency increases but some quality-adjusted life-yearsare lost. Ex post targeting, where it explicitly involves no longertreating some existing patients, can be seen in this context as aform of disinvestment [11].

Evidence Gaps: The Need for Coverage with EvidenceDevelopment and Performance-Based Risk-SharingArrangements

Another factor hindering the development and adoption of PM isthat the current regulatory and reimbursement systems are notleading to sufficient evidence as to the value of identifying andtreating only those predicted to be “responders.” Willke et al. [12]frame the PM stratification strategy in terms of “heterogeneity oftreatment effect (HTE).” In other words, different subgroupsrespond differently and PM research seeks to identify and studythis variation systematically. It is, however, often difficult toexplore this preapproval. They speculate that manufacturers arereluctant to limit market size and (potentially) increase thecomplexity of trials by looking for HTEs preapproval even thoughdevelopment costs could fall if a genetic response is identified“early enough to reduce trial sizes and increase the probability ofsuccess during Phase 3.” However, they also note that “over 50%of manufacturers have incorporated pharmacogenomics or phar-macogenetic diagnostics into their clinical development pro-grammes,” suggesting that companies are exploring this option.This is because, as Frueh [13] notes, “drug-test co-development . . .poses the least challenge with respect to demonstrating theeffectiveness of a personalised medicine approach”, and asDanzon and Towse [14] noted, “drug producers will have incen-tives to do this ... as part of the drug development process ratherthan wait for others to do it after the drugs reach the market.”

The relatively slow evolution of the science, however, meansthat this may continue to be the exception rather than the rule.Oncology and orphan diseases are areas where the science hasadvanced most, and a proactive approach to stratification toidentify HTE is likely to make sense. It does not, however, makeeconomic sense for companies to routinely invest in extensiveHTE analysis preapproval in many other disease areas unlessthey have a very strong prior view about how to stratify the trialpopulation. We agree with the central point of Willke et al. [12]that, for the time being, most HTE data are going to be collectedpostapproval, and with their argument for using “risk-sharingagreements and coverage-with-evidence development agree-ments . . . to incentivise evidence generation and utilisationof HTE.”

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Annemans et al. [8] identify “evidence gaps that are peculiarto the concept of personalised medicine.” They argue that it willbecome increasingly inappropriate to “assume perfect uptake oftests and use in terms of prescribing practice.” How patients aremanaged in practice is important. Again, postapproval datacollection will be key. They support the Willke et al. view thatcollaboration between stakeholders will be needed to increasethe efficiency of evidence collection. Risk-sharing agreementsand coverage-with-evidence development agreements provideincentives for such collaborative working.

Incentives for Evidence Generation

Frueh [13] notes that “tests developed after the drug has reachedthe market” can be difficult to introduce into clinical practicewith “slow uptake . . . in situations where the performance of thetest was less clear, differences in outcomes harder to detect, and/or the information derived from the test more difficult to trans-late into precise clinical actions.” He contrasts the slow uptake ofCYP2C19 testing for clopidogrel with the rapid adoption of HLA-B*5701 testing for abacavir. Where large studies are needed withlong follow-up periods, “it is difficult or even impossible to turnthe development cost of such tests into profits given the rates atwhich such tests are usually reimbursed.” As we have pointedout elsewhere [15], unless drug companies have an incentive tocollect evidence (as in the case of abacavir) or a payer has anincentive because the test can be cost saving, uptake for the testwill be poor. Frueh highlights “genetics for generics” as attractiveto payers, if it “significantly improves the clinical utility of ageneric drug” as in the case of testing for the use of warfarin,although Burns et al. [16] point out some of the factors that havelimited the uptake of warfarin genetic testing including lack oftest availability in rural areas, delays in the receipt of results,limited reimbursement, and concerns about cost-effectiveness,particularly in academic centers where patients can be wellmanaged by traditional means. Testing for clopidogrel, in princi-ple, falls into the genetics for generics category as well, and apayer study was initiated but unfortunately not completed.

The problem Frueh identifies is that diagnostic companies donot have incentives to collect evidence. Addressing this requireslooking at: 1) pricing; 2) intellectual property protection—forexample, a form of data exclusivity for clinical utility evidence;and 3) evidentiary standards and the costs of collecting evidence.There is a danger that high evidentiary standards in both ex anteand ex post cases of stratification will mean less PM research andtherefore fewer PM applications. Drug manufacturers have incen-tives to collect evidence in some circumstances as noted above,as do payers, although in a competitive insurer market payerswill have problems appropriating the benefits of evidence gene-ration for themselves. While it is efficient ex post that the publicgood aspects of evidence are shared, it reduces the likelihood ofpayers being willing to fund evidence generation in the firstplace. As Towse et al. [15] argue, public funding of some PMresearch will be essential.

Frueh argues that evidence standards for some tests can bemet “without evidence from a prospective trial if convincingevidence can be obtained retrospectively.”

Institutional Arrangements to Establish the Value of a Test

Frueh argues that “clinical utility for tests that follow testsalready on the market has by definition already been established”and therefore the “characteristics of the new test (in particularsensitivity, specificity, and cost) determine its performance com-pared to its predecessor and it can be judged relatively easilywhether or not the cost-benefit profile of the new test is superiorto the existing assay.” This is true, although it is important to

achieve the right balance between the gains from piggy backingon evidence generated by others and the reduced incentive fororiginators to generate that evidence if others will use it and cancompete on price without having had the expense of collectingthe evidence. Moreover, many payers do not have in placeinstitutional arrangements to assess the cost-effectiveness oftests even when evidence of clinical utility and of relative testperformance is available. As Payne and Annemans note, theNational Institute for Health and Care Excellence has establishedits Diagnostic Assessment Programme to focus on the cost-effectiveness of diagnostics, while “companion diagnostics willbe directed, via a Department of Health led scoping process,through the NICE Technology Appraisal route.” Garau et al. [17]have argued that this is an optimal institutional structure for allpayers to introduce, combined with a willingness to pay fordiagnostic tests at prices that reflect value.

Reimbursement and Pricing Flexibility to Support EvidenceGeneration

Prices that reflect value should send positive signals for invest-ment in the development of PM and in evidence to demonstrateits value. To promote a strategic approach to market access forPM, Payne and Annemans synthesize a dozen recommendationsfor Europe that vary from the positive (“To identify how com-panion diagnostics are priced and/or charged for at the providerlevel across Europe”) to the normative (“To align the use of HTAfor the reimbursement of companion diagnostics in line withexisting practice for medicines”). Their recommendations havetwo major themes: 1) we know that both evidence requirementsand reimbursement processes for companion diagnostics arehighly variable and idiosyncratic across Europe; and 2) promotingevidence generation in support of these tests will require bothgreater harmonization to send a consistent signal to test devel-opers and some reform of the health technology assessmentprocess and reimbursement rules for new medicines that couldbe targeted.

This variability and inconsistency has been noted by manyanalysts [18,19]. Broadly speaking, most individual memberstates will consider paying for new, patented medicines on theirlocal perception of “value.” However, reimbursement for com-panion tests is generally more on the basis of the expected costsof the laboratory process. In the latter case, the implication is thatno allowance is being given for the costs of either research orgenerating evidence in support of clinical utility. The standard isat best analytic validity—the test does what it is supposed to.Payers, however, want to know that as well as that the test willdeliver value when used in the health system. That requiresevidence of clinical utility.

There are now several examples of biomarker-based PM teststhat have set their prices on the basis of a value argument. But todo so, it is necessary to circumvent existing procedure-basedreimbursement systems. Oncotype DX (Genomic Health) forpredicting breast cancer recurrence (at an initial price of about$3500) could be considered the poster child in this realm. Themanufacturer of this “laboratory-developed test” (LDT) does notsell an in vitro diagnostic (IVD) kit for decentralized distribution;rather, all samples from around the world must be sent to itscentral laboratory in the United States. The development of theevidence base for Oncotype DX cost tens of millions of dollarsand used data from historical randomized controlled trials, andnew prospective randomized controlled trials are underway,including trials relying on public funding. The point is thatdeveloping a robust evidence base—as Payne and Annemans callfor—is costly, and economic incentives, including better pricingand some public funding of evidence collection, may be neededto generate it.

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Rewarding Drugs and Diagnostics for Delivering Value

If a companion diagnostic is launched in conjunction withmedicine, then it is the combination that produces the “value”in terms of health gain and cost-offsets, which are the two majordrivers in most CEAs. From an economic perspective, such acombination product is synergistic in terms of value and thedivision of value between the two is essentially arbitrary [20]. Thepayer, or its agency, for example, the National Institute for Healthand Care Excellence in the United Kingdom, could say that wewill pay the drug manufacturer the total value, and the manu-facturer can work out an arrangement to pay for and supply theassociated testing.

Arguably, this would seem to be efficient for a test-drugcombination that is launched in tandem. But there are severalpotential problems with this “solution.” First, it does not apply tothe situation where the drug is already on the market or the testis used for multiple drugs, that is, when it is a “standalone” test.Second, what if the drug manufacturer has come to market witha passable, but relatively low-quality test rather than optimizethe search for the fullest set of predictive biomarkers? We maywant a better test.

In the European Union, where the prices and reimbursementlevels for new, patented medicines are largely fixed at launch forthe remainder of a product’s patent life, there is clearly a strongfinancial disincentive for the drug manufacturer to discover anddevelop such a PM test postlaunch. We argue that flexible drugprices are needed to deal with this situation [7]. The Pharma-ceutical Price Regulation Scheme in the United Kingdom, inprinciple, allows for such flexibility, but it has not been appliedin practice, partly because of expected implementationdifficulties.

Garrison and Austin [20] and, more recently and in moredetail, Garrison and Towse [7], attempt to make the case forflexible and value-based pricing of both PMs and their accom-panying test. Still, the question remains how to divide the totalvalue created, for example, in a post–drug launch setting. Wewant to incentivize test developers to bring better tests to themarket. Garrison and Towse consider an ex post “standalone”test and argue that we could reward the drug company for thehealth gains in the responders, that is, for the mechanism ofaction that they have discovered and developed, and reward thetest developers for the value of avoiding adverse drug events thatcreate health losses and costs, as well as any premium that mightexist for the “value of knowing” due to the reduction of uncer-tainty. They recognize that “this split is arbitrary in a static sense,but it can be argued that it reinforces dynamic efficiency byconsidering the relative size of the investments needed todevelop a drug versus a diagnostic.” Clearly, this issue needsfurther analysis and testing, and vetting with stakeholders. But itis important to emphasize the need to reward both drug and testmanufacturers for greater evidence of value to encourage innova-tion. It emphasizes the importance of dynamic efficiency, asopposed to a short-term static efficiency perspective that is oftenmore about minimizing costs.

Intellectual Property Rights and Data Exclusivity

Promoting a robust evidence base may also require reforms tointellectual property rights to help to manage the market com-petition, as has been done recently for biosimilars in the UnitedStates. Market competition among PM tests is already being seen:Oncotype DX now faces several biomarker-based test competitorsthat raise fascinating and as yet unaddressed questions aboutmarket dynamics and efficient competition as well as trial designand especially how to conduct indirect comparisons amongthem. There are also incentive issues around the IVD kits versus“homebrews” (called in-house tests [IHTs] in the United Kingdom

and LDTs in the United States). IVD kits require marketingauthorization from regulatory authorities, and can be subject tosome evidence requirements. IHTs/LDTs have some qualityregulation, but evidentiary requirements are much less.

By analogy with drugs, an IHT/LDT might be seen as the“generic version” of an approved IVD. Branded medicines havepatent and market/data exclusivity that allow generic entry onlyafter a period of time (and biologicals were granted longer marketexclusivity protection). The situation, however, is different forIHT/LDTs in that they need not be exact copies of IVD kits:additional or different biomarkers may be used to predict theresponding group, and they can enter the market on the basis ofassociation data. Although Frueh rightly points out the benefits offollow-on tests entering the market, this tends to undermine theincentives for the generation of data needed for companiondiagnostics. Economic analyses of breakeven returns for bio-logicals [21] were clearly influential in setting the data exclusivityperiod for biosimilars. Similar analyses and protection for PMtests may be warranted.

Conclusions: Economic Incentives for EvidenceGeneration—Implications for Reform and Research

Promoting an efficient path to PM is going to require appropriateincentives for evidence generation so that cost-effective techno-logies can demonstrate their value to patients and to the healthcare system, and receive a return commensurate with that value.As the authors of the articles in this volume have identified, thiswill include the following:

1.

a greater willingness on the part of payers to accept pricesthat reflect value. This will involve a need for price flexibilityfor drugs as evidence of their value for different groups ofpatients emerges with evidence of HTE over time, and theneed for value-based rather than cost-based pricing fordiagnostics;

2.

consideration of some form of intellectual property protection(e.g., data exclusivity) for diagnostics to cover evidence of clinicalutility, thus enabling diagnostic companies to obtain a sufficientreturn on investment in evidence generation and ensure thatthere are incentives to bring higher quality tests to the market;

3.

realistic expectations around the standards for evidence. Thisinvolves the use of coverage-with-evidence developmentand real-world evidence collection for both drugs anddiagnostics; and

4.

public investment to complement the efforts of payers andmanufacturers, recognizing the limitations on the incentivesfor both to invest in evidence collection on all the questionsthat matter.

In conclusion, incentives matter, and the health promise of cost-effective PM is large. We need to ensure that the pricing, evidence,and intellectual property environment support those tackling thescientific challenges. This means building andmaintaining a balanceamong: 1) realistic thresholds for evidence and the need for payersto have confidence in the clinical utility of the drugs and tests theyuse; 2) payment for value, with prices that ensure cost-effectivenessfor health systems; and 3) levels of intellectual property protectionfor evidence generation that provide a return for those financingresearch and development, while encouraging competition to pro-duce both better and more efficient tests. The articles in this volumeare a useful step toward creating the right policy balance and somoving forward the opportunity for patients to benefit from PM.

Source of financial support: ISPOR provided a modesthonorarium.

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