The nature of the changes taking place within oncology makes it particularly difficult for payers to keep up, with knock-on effects for pharmaceutical companies seeking to gain market access for their latest medicines. The rate of change is such that by the time a new drug has gone through a health technology assessment process the resulting decision can easily be out of date as new evidence continually emerges. The challenge for industry is to clearly show where patient benefits reside, not only in terms of their own product but also in the context of all the other treatment options that are available at the time of assessment.
Payers are confronted with new therapies that are expensive. Their effectiveness, while often substantial, is usually seen only in a specific, small population and the evidence base available to show treatment benefit is usually limited. It’s no wonder that demonstrating the value of a new oncology therapy is challenging.
“I tend to think payers will understand the costs of a new cancer drug a lot better than they will fully understand the patient benefits it brings,” says Marc. “There’s so much happening at the moment, and the resources are probably not there to help them understand this, so in general they are likely to need to rely on other groups to simplify their decision-making processes.”
He points to CAR-Ts as one type of therapy that can only be used by a very small patient population and which has fairly high costs. Most payers are not well equipped to deal with these kinds of treatments, although Marc says he is seeing some interesting environmental shifts for payers and pharma in the US with the emergence of pay-for-performance contracts.
Whatever arrangements are put in place, the key question for the pharmaceutical industry is how to generate and synthesise evidence for the new wave of cancer treatments, both those on the market and those waiting in the wings. Traditional clinical trials are not coping well with framing the benefits of these drugs. Patient response is incredibly heterogeneous and benefits in progression-free survival endpoints are not always seen. As such, traditional models may well not work for newer immuno-oncology treatments.
“The trend here is to move away from ever-larger and longer trials and set up ones that use surrogate data. These trials may have response data and data on remission or minimal residual disease, but lack information on the long-term survival impact which is utilised in traditional modelling approaches used to demonstrate value to payers. Even something like real-world evidence, which is much discussed, is still in its infancy.”
Marc continues:
