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Precision medicine will increase drug prices and that’s a good thing

Precision medicine may also lead to more novel reimbursement structures, such as indication-specific pricing and performance-based pricing.

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Precision medicine provides better targeting of treatments to patients who really benefit from them. Fewer people will be treated with a given drug, compared to standard therapies, but everyone is better off – the treated have better outcomes, and everyone else can move on to another therapy that works for them. In a world where prices are increasingly based on value, this means that drug prices are likely to rise, but overall spending on pharmaceuticals may not change, or could actually fall.

Precision medicine offers patients the promise of identifying whether a medicine will work for them before they ever take it. Most often, precision medicine is based on genetic sequencing information about the patient, or in the case of cancer treatments, the genetic information of their tumor. By tailoring treatments to each individual based on their clinical and genetic characteristics, the outcomes of patients using precision medicines are likely to improve dramatically.

One quintessential example of the use of precision medicine is Herceptin (trastuzumab). Herceptin is a treatment for breast cancer used exclusively among patients with HER2 genetic mutation. Patients that are HER2-positive benefit from precision medicine as they can expect superior outcomes from Herceptin relative to the previous standard of care. However, HER2-negative patients also benefit from precision medicine. Without knowledge of whether the patient has the HER2 genetic marker, patients would have to take Herceptin to learn that they are non-responders – leading to unnecessary side effects, wasted resources, and most importantly, lost time. Despite these clear benefits to patients, however, precision medicine does create some challenges for regulators and payers.

For regulators, precision medicine may up-end the standard FDA approval process. Currently, drug approval is conditioned on a multi- phase clinical trial process, culminating in a relatively large Phase III randomized controlled trial to demonstrate efficacy and safety. As precision medicine targets a narrower set of patients, the patient populations eligible for these clinical trials will be much smaller. With a small population, there may be cases where there is insufficient sample size to power a Phase III clinical trial for a given precision medicine. A number of alternatives have been proposed to the standard Phase III trial approach including adaptive trial designs, additional post-market surveillance and even N of 1 trials. The FDA already has a number of accelerated approval processes—such as its Breakthrough Therapy designation—and this can help speed precision drugs to market.

Precision medicines also present a challenge to payers, as the price of many drugs in this category are likely to be high. To understand why this is the case, consider a simple example where there are 100 patients with a disease; half of patients with a given disease have the genetic mutation A and half do not. Further, assume that the treatment improves survival by 4 years for patients with mutation A, but the treatment has no effect on survival for those without the mutation. Thus, the average survival benefit is 2 years.

If the value of one year of life is $100,000, then the price of the medicine based on its value to treated patients should be $200,000 (2 x $100,000). However, under the precision medicine framework where physicians could identify patients with mutation A and would only provide the treatment to these individuals, then the average survival gain would be 4 years, and the price of the treatment would be set at $400,000 (4 x $100,0000) if pricing is based on the clinical benefit of those treated. Thus, although patients have better outcomes—particularly patients without mutation A who can now try other medications—the drug’s cost per unit price doubled.

Although precision medicine may lead to higher drug prices, its effect on total drug spending is unclear. Let us return to our simple example. Without precision medicine, assume 100 people receive the medication at $200,000 for a total of $20m (100 x $200,000) in annual spending. With precision medicine, only the 50 people with mutation A receive the medicine at a cost of $400,000 leading to an identical $20m (50 x $400,000) in drug spending. Thus, the use of precision medicine may increase, decrease, or have no effect on total drug spending, depending on the size of the responder population, the degree to which treatment response is clearly identified by biomarkers, the cost of diagnostic tests, market forces, policy changes and of course a large number of other factors.

Precision medicines may also lead to more novel reimbursement structures, such as indication-specific pricing and performance-based pricing. Value-based pricing is particularly useful in the case of precision medicines as the clinical trials for this medication are likely to have smaller sample sizes and thus payers will have much more uncertainty regarding whether the drugs are likely to work well in the real world. Rather than paying a fixed fee for a drug based on volume, payers may reimburse innovators based on whether patients achieve specific outcomes such as halting tumor progression, specific survival targets or other outcomes. In my current role as director of research for the Innovation and Value Initiative, we are working to address just these types of questions surrounding value measurement and reimbursement.

Another complication for payers is that diagnostic testing will become more valuable. When pharmaceuticals were used to treat all people with a disease, diagnostic testing was of low value relative to the drug itself. However, since precision medicine diagnostic testing can help identify which patients would benefit from treatment, it can greatly reduce the number of individuals who would use a treatment. Thus, one could imagine that precision medicine could result in a variety of pricing mechanisms for a diagnostic test. The simplest would be to bundle the diagnostic test with the treatment. In my example above, payers would not be charged for patients for whom the test proved negative.

For patients for whom the drug worked (those with mutation A), the cost of the diagnostic test-drug combination would be $400,000, but the cost would be $0 for those for whom the treatment would not work (those without mutation A). An alternative value-based pricing approach would be to charge $200,000 for each diagnostic test and give the drug away for free for those who had mutation A. In both cases, total spending on the diagnostic and drug would be $20m in our simple patient example with 100 people, but clearly reimbursement structure for the diagnostic varies dramatically.

Despite these challenges for regulators and payers, precision medicine clearly has enormous potential to improve patient outcomes by ensuring that patients who would benefit from a treatment receive that treatment and those who would not benefit can quickly explore other treatment options.


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Jason Shafrin

Jason Shafrin, PhD is a Vice President at PRECISIONheor and an expert in value-based purchasing methods, quality of care measures, discrete choice experiment survey design, health insurance claims data analysis, and digital medicine. Dr. Shafrin has not only developed reports to advise government agencies such as the Institute of Medicine and Centers for Medicare and Medicaid Services (CMS), but his research has also been published in leading journals of economics, medicine, and health policy including the Journal of the American Medical Association, Health Affairs, Health Economics, Health Services Research, and Pharmacoeconomics among others. Prior to joining PRECISIONheor, Dr. Shafrin served as a policy lead for Acumen, LLC. In this role, Dr. Shafrin helped the Centers for Medicare and Medicaid Services implement a number of provisions of the Affordable Care Act, including the design of value-based purchasing programs for hospitals, physicians, and home health agencies. Dr. Shafrin is also the founder and editor of the Healthcare Economist blog.

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