To the extent that some precision medi-
cines reveal themselves to be curative, it is
not clear how the current payment system
will absorb their costs. Due to churn in in-
surance markets, the insurer that pays for
therapies, often expensive and administered
during a short window of time, and the in-
surer that saves from future reductions in
expenditures (most likely Medicare) are un-
likely to be the same, creating a disconnect
between coverage and long-term value (11).
An inability for insurers and patients to pay
for such drugs will reduce firms’ incentives
to develop them. Economists have argued
for new financial instruments, which would
function like mortgages to spread the costs of
high-value, high-price treatments over time,
decreasing the upfront financial burden for
patients and payers (11, 12). Such schemes in-
crease the value of insurance, which in turn
increases demand for treatments and leads
to higher prices, although not automatically
to higher spending. Such instruments will
be more effective when combined with out-
comes-based contracts and/or clinical trials
establishing a treatment’s effectiveness.
Closely related to the idea of spreading out
the cost of precision medicines over time is
the idea of spreading out their cost over more
individuals. Precision medicines may require
larger insurance pools to spread payments
for these treatments, especially for employer-
provided insurance, which covers more than
half of Americans. Employers of all sizes will
struggle to absorb these costs, but smaller
employers would be devastated by the pres-
ence of a single employee who needed access
to a million-dollar curative therapy. Publicly
financed “high-risk pools” may help cover
high-cost therapies. In such pools, the gov-
ernment uses tax revenues to cover certain
precision therapies (assuming that it is possi-
ble to raise taxes or find revenues elsewhere).
This would be analogous to “catastrophic”
reinsurance plans for Medicare Part D (13).
Alternatively, policies that decouple insurance from specific firms—by encouraging employers to purchase insurance on
exchanges where multiple employers pool
patients—would help spread risk. In the
absence of larger insurance pools, insurers will have an incentive to cherry-pick
patients with a low propensity to need
precision therapies. Although “spreading
the risk” via a larger insurance pool may
lower per capita premiums, this is different
from reducing the price of a therapy, which
would still be high.
Finally, the high effectiveness of precision medicines means that payment decisions based on comparative effectiveness
are unlikely to reduce the budget pressure
from these therapies; cost-effectiveness will
have to be considered (14). Creating price
competition can provide financial relief for
patients and payers. This includes policies
to expedite biosimilar review times at the
FDA and to encourage physicians to actively
prescribe biosimilars when there is clinical
evidence that they are effective substitutes
(even in the absence of automatic pharmacy
substitution). It also will mean stimulating
more brand-brand and biologic-biosimilar
competition, which depend on payers’ ability
to use formularies and competitor products
in those formularies. These policies require
a richer understanding of the process by
which companies bring drugs to market.
Clear characterization of the precision medicine development pipeline—including its
sensitivity to economic incentives such as
exclusivity periods, effective patent length,
public funding, and the roles of early stage
companies and more mature players—will
allow policy-makers to more accurately anticipate the likely profiles of medicines that
will reach the market in years to come (15).
Despite the potential link between the high
price of precision medicines and lower access
to them, establishment of genomic databases
and validated biomarkers is expected to de-
crease the cost of trials and time-to-market by
allowing smaller, more focused clinical stud-
ies, particularly in the more expensive, later
phases of development. Platform trials that
incorporate multiple therapies and biomark-
ers with a common control arm can provide
significant efficiencies to test and prioritize
promising therapeutic hypotheses (16).
Layering Bayesian adaptive techniques
that use information as it accumulates to
focus resources on the most promising
arms and to investigate surrogate endpoint
relationships in real time offers even more
potential. The most notable example is the
I-SPY 2 trial of neoadjuvant experimental
therapies in breast cancer, which recently
identified two therapies with biomarkers
that have a high probability of success in
phase III studies (17–19).
Reductions in both the cost and length of
trials mean that more drugs can clear the
hurdle of commercial viability. A lower hurdle for commercial viability will lead to more
innovation, which can, in turn, create more
competition. But smaller and shorter trials
necessitate a strong and highly engaged, appropriately resourced FDA, working at the
cutting edge of regulatory science. This reinforces the importance of state-of-the-art
regulatory science and policy in facilitating
precision medicine development.
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