“Clinical utility indicates the degree to which the pharmacogenomic marker will improve health outcomes, so is essentially equivalent to the ‘effectiveness’
component of ‘cost–effectiveness’.”
Michael J SorichAuthor for correspondence: Department of Clinical Pharmacology, School of Medicine, Flinders University, Bedford Park, Adelaide, South Australia, 5042, Australia Tel.: +61 8 8204 6682 Fax: +61 8 8204 5114 [email protected]
Michael D WieseSchool of Pharmacy & Medical Sciences, University of South Australia, Adelaide, Australia
Brita PekarskyBaker IDI Heart & Diabetes Institute, Adelaide, Australia
Cost–effectiveness of genotyping to guide treatment
For a number of years pharmacogenomics has held substantial promise with regard to improving use of drug therapy, but meaningful translation to the clinic has generally been disappointing. One of a number of important criteria that influences this successful translation is the cost–effective-ness of routinely assessing pharmacogenomic markers prior to initiating drug therapy [1,2]. Here we will highlight and briefly explore four key issues relating to the cost–effectiveness of pharmacogenomics:
�� The relationship between clinical utility and cost-effectiveness;
�� Cost–effectiveness and the prioritization of pharmacogenomics research;
�� Time dependency of cost–effectiveness estimates;
�� Whether pharmacogenomics will reduce costs to the healthcare system.
Relationship between clinical utility & cost–effectivenessCost–effectiveness is routinely determined by a cost–effectiveness analysis (CEA), which involves construction of a model that incorporates clinical data (clinical utility), alternative treatments and the cost of various healthcare services and inter-ventions, including the procurement cost of the drug and the cost of performing the pharmaco-genomic test. For a published CEA to be useful in decision making, an audience needs to assess its face value. The modeling methods typically utilized in CEAs may be difficult for clinicians and pharmacogenomics researchers to follow, and within each CEA there are a number of funda-mental assumptions or limitations that might not be apparent. Clinical utility is a key characteristic underlying the success of a pharmacogenomic
KEYWORDS: clinical translation n clinical utility n cost–effectiveness n evidence n pharmacogenetics n pharmacogenomics n prioritization
marker, and we have found that the relationship between clinical utility and cost–effectiveness is an important aspect of assessing the face value of CEAs. Clinical utility indicates the degree to which the pharmacogenomic marker will improve health outcomes, so is essentially equivalent to the ‘effectiveness’ component of ‘cost–effectiveness’. When clinical utility is well established, CEAs can help decision-makers assess whether subsidy of a pharmacogenomic marker is a good invest-ment. However, it is well-recognized that the development of high-quality evidence supporting the clinical utility of pharmacogenomic markers is an ongoing major challenge [3,4]. If the CEA indicates that the marker is cost-effective but you are not convinced of the clinical utility, then the underlying assumptions should be carefully scru-tinized to understand how the effectiveness of the pharmacogenomic marker (i.e., clinical utility) has been estimated [5,6].
“More formal analytical methods called ‘value of information’ analyses may be used to quantify the value of undertaking further
research of a specific pharmacogenomic marker, and thereby potentially guide the
prioritization of research efforts.”
Prioritization of pharmacogenomics researchAlthough many existing CEAs of pharmaco-genomic markers are limited by the evidence sup-porting their clinical utility, they may still be of value as exploratory ‘what-if’ analyses. Rather than informing decisions on whether the pharmaco-genomic marker should be funded for clinical use based on current evidence, a CEA based on limited evidence of clinical utility is of more use to pro-vide insight into the value of undertaking research part of
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Editorial Sorich, Wiese & Pekarsky
to further develop the marker and the evidence supporting its clinical utility [7]. For example, if there is a pharmacogenomic marker for which the degree of clinical utility is uncertain, a CEA could be used to estimate the cost–effectiveness of the marker under a range of plausible values for the clinical utility and price. If the cost–effectiveness estimate of the marker is unfavorable under most plausible scenarios, additional research is unlikely to be of substantial value, but if sensitivity analyses show that a marker will be highly cost-effective under most reasonable scenarios, then under-taking research required to further develop the marker may be worthwhile.
“The rapidly changing cost and clinical utility landscape further highlights the need for innovative approaches for developing the
evidence supporting promising pharmacogenomic markers in a timely
manner.”
Although there are many pharmacogenomic markers that have preliminary data supporting their potential value, developing high-quality evidence supporting the clinical utility and cost–effectiveness can be both time-consuming and expensive. More formal analytical methods called ‘value of information’ analyses may be used to quantify the value of undertaking further research of a specific pharmacogenomic marker, and thereby potentially guide the prioritization of research efforts. This method can also be used to assess alternative research options for develop-ing the evidence supporting a specific pharma-cogenomic marker. A recent example was a value of information analysis of cytochrome P450 (CYP)2D6 genotyping to guide tamoxifen treat-ment in breast cancer [8]. This study identified that researching the relationship between CYP2D6 genotype and tamoxifen effectiveness would con-tribute the greatest value, and that value to the UK healthcare system of removing uncertainty of the genotype effect (GB£53–£82 million) would likely exceed the research cost [8]. The use of value of information analysis to date has been relatively limited despite its considerable potential to help guide pharmacogenomics research.
Time-dependency of cost–effectiveness estimatesIt is important to be aware when reading published CEAs that, even if there are no further develop-ments in the evidence for a specific pharmacog-enomic marker, the cost–effectiveness of utilizing the marker may change markedly over time [9].
Costs of drugs often make a major contribution to the overall costs, and these can change sub-stantially when a patent expires or a new drug enters the market. For example, screening for the HLA-B*1502 allele (which is predictive of serious skin reactions to carbamazepine and phenytoin) in newly diagnosed epileptic patients has been shown to be cost–effective in specific ethnic populations in Singapore [10]. Although the CEA was insensi-tive to the price of carbamazepine and phenytoin, it was sensitive to the price of the newer and more expensive alternative antiepileptic agents that are not associated with serious dermatological toxici-ties. Other examples include the impact of new oral anti-coagulant drugs on the clinical utility and cost–effectiveness of screening CYP2C9 and VKOR1 genotypes prior to initiation of warfarin, and the impact of the introduction of ticagrelor and generic clopidogrel on the cost–effectiveness of screening for CYP2C19 genotype prior to the use of clopidogrel [7]. It is also relevant to consider that the use of drugs and treatment approaches often evolve in parallel with pharmacogenomic research. In the context of early rheumatoid arthri-tis, despite no new first-line agents becoming avail-able, there have been ongoing improvements in how these first-line drugs are used clinically, most notably moving from sequential monotherapy to more aggressive multidrug strategies [11]. Although research into pharmacogenomics of rheumatoid arthritis has been prominent in recent years [12], there has been little improvement in predictabil-ity of disease outcome over that which has been provided by the parallel increase in clinical disease assessment [13]. Finally, it may also be pertinent to consider whether the cost of the test itself may change over time, and the impact of alternative testing strategies that may become more promi-nent in the future (e.g., whole genome stored in an electronic health record). The rapidly chang-ing cost and clinical utility landscape further highlights the need for innovative approaches for developing the evidence supporting promising pharmacogenomic markers in a timely manner.
Whether pharmacogenomics will reduce costs to the healthcare systemAdvocates of pharmacogenomics highlight the potential to decrease costs. For instance, if a phar-macogenomic marker can identify individuals who do not benefit from use of a high-cost drug, there is the potential to reduce costs by avoiding wastage. Similarly, if a pharmacogenomic marker can help prevent toxicity then there is the poten-tial to avoid costs (e.g., hospitalization) associated
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Cost–effectiveness of genotyping to guide treatment Editorial
with that toxicity. In reality, however, a number of factors can moderate these potential savings. The savings from treating less people with a new drug because they are identified as potential poor responders may be offset by the costs of alternative treatment they will receive. For new drugs that are substantially more efficacious than previous treatments, clinician uptake could be low for a test that identifies a group of patients for whom the drug is less effective compared with other patients. This is likely to be particularly prominent if, in the group with the relatively poorer response to the new drug, the new drug is more effective (but less cost-effective) than the available alternative treatments.
“The challenge is to develop policies that facilitate the translation of
pharmacogenomic research into the clinic by striking a balance between profits to
incentivize commercial development of pharmacogenomic markers and cost savings
appropriated by the healthcare system.”
Even if a pharmacogenomic marker generates treatment savings without loss in effect for patients changing their therapy, the share of these savings that are appropriated by the healthcare system depends upon the dynamics of drug and assay prices. New drug prices are not based on sim-ple mark-ups over the cost of production, but by market forces (competition) and reimbursement processes (for example, decision thresholds such
as those used by the National Institute for Health and Care Excellence in the UK). For drugs that are co-developed with a pharmacogenomic marker (e.g., Herceptin®), the expected reduction in vol-ume of sales may be offset by increasing the price charged [14]. Cost savings might also be appro-priated via the price charged for undertaking the pharmacogenomic assay. If the assay is patented or technically complex, the manufacturer may price this at what the market will bear, rather than a conventional mark up. An example is the OncotypeDx® assay for early breast cancer, which is a relatively complex gene-expression profile that is priced at a few thousand dollars per test [15]. Thus, any savings generated from avoiding chemo-therapy are at least partially offset by the cost of undertaking the assay. The challenge is to develop policies that facilitate the translation of pharma-cogenomic research into the clinic by striking a balance between profits to incentivize commercial development of pharmacogenomic markers and cost savings appropriated by the healthcare system.
Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a finan-cial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
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