It sounds like something out of a dystopian novel, but it’s true. People can now place bets on clinical trial outcomes and FDA regulatory decisions like they would bet on the Super Bowl or Kentucky Derby.
Last week, prediction market company Kalshi announced that users will now be able to place bets on events like whether a drug hits its primary endpoint or whether it gets approved.
The company, in partnership with data firm AppliedXL, launched the markets as a pilot program, starting with roughly a dozen contracts tied to late-stage trials from established pharma companies. For instance, you can bet on when the FDA will approve Takeda Pharmaceutical’s oveporexton, Intellia Therapeutics’ lonvo-z, and Eli Lilly’s retatrutide and VERVE-102. You can also bet on when Intellia Therapeutics will submit a biologics license application for lonvo-z and when Compass Pathways will submit a new drug application for COMP360 psilocybin.
Kalshi is painting the move as a transparency play, saying the move could surface pharmaceutical information typically not made public.
Not everyone is convinced, though. One healthcare executive — Shashi Shankar, CEO of Novellia, a platform that helps patients consolidate medical records and share anonymized data with drugmakers — is worried the model invites exactly the kind of insider trading regulators have already seen on prediction marketplaces like Kalshi.
Just last week, news reports emerged saying a teleprompter operator made six figures on Kalshi betting on speeches he had advance copies of. And earlier this year, a Special Forces soldier made more than $400,000 on Polymarket betting on a raid he knew was coming.
“I think at best it incentivizes predictably bad behavior from folks with insider access, and at worse, it treats a patient’s illness like a coin flip and dehumanizes what it means to live with a serious or complex condition,” Shankar declared.
He noted that a Phase 3 clinical trial typically involves several hundred people — biostatisticians, data and safety personnel, site coordinators, sponsor staff and so on.
“If employment verification couldn’t stop a guy running a teleprompter, I’m not sure how it’s going to stop someone who already knows the numbers within a massive drug development program. This isn’t to knock folks involved with drug development, but to ignore that very likely outcome is foolish,” he remarked.
But Shankar’s concerns extend beyond compliance. He’s uncomfortable with what the putting trials on betting markets will do to the meaning of the data itself.
Shankar pointed out that trial results aren’t just numbers — they represent whether someone’s cancer responded, how their rare disease is progressing or whether a parent lives long enough to attend their child’s graduation or wedding.
Patients agree to share that data because they’re told it will fuel medical progress for people like them, and turning it into a “yes” or “no” contract for a stranger with no connection to that patient undermines the entire premise, he argued.
Amy Bucher, chief behavioral officer at patient engagement startup Lirio, raised a different concern. She said prediction markets could influence the behavior of people working on the trials.
Once a prediction becomes public, it becomes part of the environment that researchers, patients and sponsors operate in — and behavioral research shows that expectations can quietly influence attention, interpretation and decision-making, often without people realizing it, Bucher explained.
“My concern isn’t that scientists would suddenly act unethically because a prediction market exists. Most researchers are deeply committed to scientific integrity. But humans are susceptible to cognitive biases, social influence and incentives,” she stated.
When there are publicized expectations related to whether a trial will succeed or fail, these expectations could influence how the people involved in the trial allocate funding and interpret ambiguous findings, as well as what evidence they pay attention to and how they communicate about the results, Bucher said.
This begs the question of expertise, as many participants in prediction markets may have little scientific or clinical training.
“Their judgments may be based on incomplete information, market sentiment, media coverage or broader beliefs rather than a deep understanding of the underlying biology. If these markets begin influencing investment decisions, public perceptions or organizational priorities, we should be thoughtful about whether the signal is actually reflecting scientific evidence or simply aggregating opinions,” Bucher remarked.
Kalshi’s pilot is small by design. Whether the concerns being raised about insider access and trial integrity scale with it remains to be seen.
Photo: Eugene Mymrin, Getty Images
