Could Medicare Drug Price Cuts Halt Treatments for Cancer or Alzheimer’s?

The federal government has negotiated directly with drug companies for the first time on the prices of a small number of drugs. The new prices, announced in mid-August, will take effect in January 2026 and will help the Medicare program cap the amount an individual patient pays out-of-pocket for prescription drugs at $2,000 per year.

This historic policy, which had been floating around for decades, was long met with opposition from “Big Pharma” until Congressional Democrats passed the Inflation Reduction Act in 2022 and President Joe Biden signed it into law.

Pharma has tried to block the negotiation policy in court after it became law. Their concerns, that these “price controls” will stifle innovation, have been echoed by Republicans and policy commentators since the recent finalization of the negotiation prices. Companies like Pfizer and Merck argue that if profits are reduced, it will be harder to hire scientists, invest in lab space, and set up clinical trials to test future drugs.

It’s a dire proposition. In trying to control drug costs for 67 million Medicare patients today, we could inadvertently block the development of future drugs that could save lives. We don’t explicitly say it, but we implicitly put at risk the cure for cancer, Alzheimer’s, or other incurable diseases.

But we have good reason to believe that current policies will not have such a trade-off anytime soon. First, pharmaceuticals are incredibly profitable, and while these negotiated prices could potentially cut into profit margins, they cannot completely stifle the incentive to innovate, as several key studies in the industry have shown. Second, if we are concerned about future innovation, we should focus on lowering the cost of drug development. This is actually one area where AI holds promise. By identifying the best candidates for possible treatments early in the research process, we can accelerate development and continue to reduce costs without missing out on tomorrow’s breakthroughs.

The argument for diminishing returns usually goes like this: pharmaceutical companies spend a lot of money developing drugs, including drugs that never make it to market because they are not proven effective. When there is a new, effective drug to sell, they need to make enough money to cover the development costs, and then they need to make more money to make a profit and invest more money in research and development for the next generation of drugs.

Other wealthy countries, such as Australia and the UK, use the central role of government in their health care systems to drive down prices while fostering their own medical innovation sectors. But in the US, before the IRA provisions became law, prices were left more to the free market and the individual bargaining positions of manufacturers, private insurers, government, and pharmacy benefit managers. A variety of rebates, rebates, and other funding mechanisms often obscured and increased drug prices for Americans. As a result, the US pays the highest drug costs in the world.

As a result of the amount we pay, Americans generally get first access to new treatments. But that early access is only useful if patients can afford the drugs. Too often, they can’t.

But the problem is that this entire premise is flawed. When the Congressional Budget Office evaluated the bill before it was passed, analysts said they did not expect it to have a significant impact on future drug development. According to an analysis published in 2017, the need to cover R&D costs does not really explain the high cost of drugs in the United States—at least not entirely. Health ProblemsJournal of Health Care Research.

A study by Nancy Yu, Zachary Helms, and Peter Bach of Memorial Sloan Kettering Cancer Center determined the excess price paid by the United States compared to other wealthy countries. They called this price the U.S. R&D “premium.” They then calculated how much revenue the premium generated for the world’s top 15 pharmaceutical manufacturers and compared that to the companies’ R&D spending.

They concluded that the average list price of drugs in other countries was 41% of the net price paid in the United States. Big Pharma made $116 billion in profits from these excessive U.S. prices in one year. In the same year, pharmaceutical companies spent $76 billion on R&D. These figures suggest that pharmaceutical companies can afford to avoid such premiums. “Even if we covered the entire global research budget, there would be billions of dollars left over,” the authors wrote.

At some point, the expectation of declining profits may begin to dampen the industry’s willingness to invest in new drugs and take risky gambles that could potentially yield big profits. But are we getting close to that point? Whatever these companies object to, it may be more meaningful to look at what they do rather than what they say.

Last year, Richard Frank and Lo Huang of the Brookings Institution looked at business decisions made by pharmaceutical companies since the arbitration clause became law. The researchers looked specifically at mergers and acquisitions, which are another way big pharma companies find new drugs (usually by buying promising startups that have already done R&D).

Frank and Huang found little evidence that pharmaceutical companies expected a significant hit to sales as a result of the change in negotiation process. Rather, they found an increase in drug deals in the early and late stages of testing. Overall M&A spending was not significantly different, and some recent earnings reports expressed optimism about the future.

This makes sense. The IRA stipulates that Medicare’s negotiating authority is limited and phased in gradually. In the first year, Medicare can choose 10 drugs to negotiate. The next year, the program can add 15 more, and the year after that, 15 more.

There is good reason to think that we can afford lower prices for more drugs. But it would still be nice if we could develop drugs faster and therefore cheaper. Then we could naturally lower prices and get new drugs to people who need them. It’s a win-win.

There may be ways to streamline the approval process and criteria for more drugs. In his newsletter, writer Matt Yglesias discusses some options for Congress and the FDA to consider, including accepting more data from clinical trials conducted in other countries (where less expensive trials are often possible).

But science is the biggest obstacle to new drugs. It can take years for researchers to figure out how a disease works, its biological basis, and hypothesize candidates for intervention. It can take decades to move from basic research that reveals these components to clinical trials that secure FDA approval. The FDA only considers something once it has figured out what actually works. That’s why big pharma spends so much on acquisitions. Even with all the resources, there’s no guarantee that in-house scientists will find a promising treatment candidate before outside researchers.

The best way to maximize our R&D resources and get the most bang for your buck when setting up expensive human trials is to identify the most promising candidates in the first place. But we’re dealing with a huge amount of information—a library of all human genetics. That’s why drug developers are turning to AI to help them sort through it.

Leading researchers in antibiotic resistance have trained computers to find promising molecules for treating bacteria that are difficult to treat with traditional medicine, even in the DNA of extinct animals. Longevity advocates have similarly placed their faith in AI. Startups like Recursion Pharmaceuticals, which STAT profiled, have built entire businesses around using AI to find potential drug candidates, including drugs on Big Pharma’s shelves that could be repurposed for new diseases.

It’s unclear whether that AI ambition will pay off, but it’s another reason to be optimistic.

Too often, the drug pricing conversation is framed as either one or the other: lower prices or find new treatments, but not both. That’s the wrong choice.