STAT+: Pharma’s drug spending deflection

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PhRMA doesn’t want you to look at drug spending

Lobbying groups frequently send news releases and talking points to journalists as a way to influence reporting. Right now, the pharmaceutical industry’s primary lobbying shop, PhRMA, wants everyone to ignore the clear-cut rise in prescription drug spending. 

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<![CDATA[Phase 2 ZEPHYR finds oral betovumeline combo reduces PANSS and improves cognition in acute schizophrenia, with generally mild side effects.]]>

OpenAI called the Hugging Face attack unprecedented. But we’ve been here before. 

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got genuine chills about what large language models are now able to do. But this is a case of human hubris, not rogue AI.

I am not an alarmist. In fact, I have been pushing back against AI scare stories for years. Even so, this incident crossed a line. I think it’s the clearest illustration yet of how the people building and testing this technology do not fully understand what they’re doing. OpenAI could—and should—have seen this coming.

Here’s what happened, at least according to the two companies involved. A couple of weeks ago, OpenAI started testing the hacking abilities of some of its new models, including GPT‑5.6 Sol (released in June) and what OpenAI describes as “an even more capable pre-release model.”

OpenAI pitted its models against a benchmark called ExploitGym, released in May, which challenges LLMs to find ways to exploit real-world vulnerabilities found in commonly used software.

To see what they could do, the researchers removed most of their cybersecurity guardrails. Then they ran the models inside a sandbox that was cut off from the internet except for one link to a third-party piece of software that acted as a proxy to the outside world, and let them install code that they needed to beat ExploitGym.

On July 9, according to reporting by Reuters, OpenAI’s models started trying to break through the proxy. They found an unknown bug in the proxy’s software and used it to access the internet. From there, they broke into Hugging Face’s computer systems on July 11, apparently looking for data sets and solutions that would help them complete their task. Hugging Face announced the hack on July 16. 

OpenAI did not realize (or at least did not reveal) that its models were involved until July 21, around 10 days after they broke containment and a week after Hugging Face had shut down the attack and alerted the FBI.

In a statement given to MIT Technology Review, OpenAI says: “We are conducting a thorough review along with external advisors and with oversight from our Safety and Security Committee. Once the review is complete, we will publish a technical report of our learnings for everyone.” The firm also confirmed that its researchers were properly using existing safety guidelines and procedures at the time.

Wake-up call

OpenAI has said the event was unprecedented—and in many ways it was. This was the first time outside of a simulation that LLMs escaped what was thought to be a secure sandbox, accessed the open internet, and attacked an unrelated organization. It’s a wake-up call that shows just how good the latest LLMs are at finding and exploiting vulnerabilities in real-world software with little or no human guidance.

And yet at the same time, what OpenAI’s models did is something this technology has done for years. Give a model a goal and it will very often achieve that goal in unexpected ways, finding loopholes that look like cheats. OpenAI itself has studied this behavior.

A decade ago, it shared results of an experiment in which a model was tasked with beating a video game called CoastRunners. Human players take it for granted that the way to do this is by racing a boat through a series of flags to the finish line, racking up points for each flag you hit. OpenAI’s model figured out that you could get a high score by spinning in a circle and hitting the same three flags over and over again. There have been dozens of similar examples from researchers since. AI will always find a way.

“Despite repeatedly catching on fire, crashing into other boats, and going the wrong way on the track, our agent manages to achieve a higher score using this strategy than is possible by completing the course in the normal way,” OpenAI wrote in a blog post about the CoastRunners experiment in 2016. “While harmless and amusing in the context of a video game, this kind of behavior points to a more general issue … it is often difficult or infeasible to capture exactly what we want an agent to do.”

I couldn’t help thinking about CoastRunners when I read OpenAI’s blog post about the Hugging Face attack: “All evidence suggests that the models were hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal … After gaining internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

Last week’s news was not about rogue AI, despite the headlines. It was about models achieving the goal they had been given: Find ways to exploit vulnerabilities in software. The fact that those models then behaved in a way OpenAI had not anticipated isn’t surprising. But it is worrying.

Back in 2016, OpenAI had this to say about its CoastRunners bot: “More broadly it contravenes the basic engineering principle that systems should be reliable and predictable.” A decade on, those basic engineering principles are still AWOL.  

<![CDATA[New analysis shows COMP360 psilocybin sessions for PTSD stay mostly silent, with nondirective support boosting safety and autonomy.]]>

How lasers could help provide fuel for nuclear reactors

Outside the small town of Paducah, Kentucky, a wealth of uranium is locked away in thousands of storage cylinders filled with waste material from a now-closed nuclear enrichment facility. Lasers could help get it out.

A company called Global Laser Enrichment (GLE) is looking to reprocess this old material with a new technology called laser enrichment. It could be more efficient than conventional enrichment methods, allowing the company to refresh the material and produce feedstock at the same concentration as a natural mined source. And in the future, the company claims, laser enrichment could be used to make material for nuclear fuel, including the kind used in advanced reactors.

Nuclear power provides about 9% of global electricity today, and that fraction could tick up as major world powers like the US and China look to build new reactors, including some based on next-generation technology. New, cheaper methods to obtain fuel could help ensure that those nuclear projects stay on track.

Naturally occurring uranium is largely made up of uranium-238 (over 99%) and uranium-235 (about 0.7%). Uranium-235 is the fissile type, meaning that, when hit with slow low-energy neutrons, it can sustain a chain reaction that generates electricity. So reactors generally use material with a higher concentration of U-235 than what’s pulled from the ground. Today’s conventional reactors usually use low-enriched uranium, typically is about 5% U-235, though some advanced reactor designs will use fuel that’s up to 20% U-235.

Today, centrifuges are the dominant tech used to enrich uranium. The equipment essentially takes uranium-containing material and spins it around incredibly quickly, so the heavier material (which contains U-238) spins out to the edge, while the lighter material (which has U-235) stays closer to the center. (If you’ve ever swung a mustard bottle to get the last of it out, you’ve used the same basic idea behind a centrifuge.) Then the material that has a higher concentration of U-235 can go on to be made into nuclear fuel.

Laser enrichment, on the other hand, takes advantage of the fact that all molecules vibrate and rotate at an atomic scale in ways that depend on their specific material. Even different uranium isotopes have distinct fingerprints.

Lasers are so precise they can target one particular material (like molecules that contain U-235, for example). If you shine a laser at a mixture, you can selectively excite just the material you’re targeting, giving it a bit more energy. This changes the way it behaves, which can make it easier to separate out the material you want using chemical or physical methods.

A wide range of separation approaches have been developed in research and industry. Some aim to electrically charge U-235 atoms, allowing them to be moved with electrostatic or magnetic fields. Others change how the material reacts chemically. 

The details of GLE’s specific technology are classified, and company officials declined to share how the process works. 

There’s been interest in using lasers for uranium enrichment for decades, says Charles Forsberg, a principal research scientist in nuclear science and engineering at MIT. 

However, in their early days lasers tended to be high-maintenance, unstable and difficult to operate. They’ve improved dramatically, making laser enrichment a more attractive prospect than it was during the early research.

Even more than technological improvements, a recent geopolitical shift could boost new enrichment technology. Russia has the largest uranium enrichment ecosystem in the world, and the country has historically dominated the market. “Nobody in the West was going to build a new enrichment plant while the Russians flooded the world with enriched uranium,” says Forsberg. 

Since the start of the Ukraine war, however, countries including the US and UK have taken steps to limit or ban imports of Russian uranium. That’s opened the door for companies to set up new enrichment operations, including some that use new technologies, Forsberg says.

Demand for fuel is increasing as countries look beyond Russia for uranium supply. “The gap is just becoming bigger and bigger, and this technology is right in the middle,” says Christo Liebenberg, president of LIS Technologies, one of the companies aiming to build laser enrichment capacity in the US.

LIS Technologies was founded in 2023, and the company recently purchased a 200-acre site in Oak Ridge, Tennessee. It’s currently in the pre-application process with the US Nuclear Regulatory Commission for its facility. The company plans to take in natural-grade uranium and make a product that’s roughly 5% U-235, though it hopes to eventually make more concentrated material that can be used as fuel for next-generation reactors.

GLE is taking a different approach: Rather than using its technology to enrich freshly mined material to the 5% concentration that can be used in fuels, it’s hoping to start by rehabilitating old waste.

The company has a contract with the US Department of Energy to reprocess waste material at the enrichment site in Paducah. The facility could enrich up to 200,000 metric tons of material that contains small amounts of uranium leftover from an older enrichment process.

GLE is taking the material that’s at least 0.25% U-235 and enriching it to about 0.7%. That material can then be further processed and slotted into the uranium supply chain in place of freshly mined material. “It’s kind of like a large aboveground uranium mine for us,” says Nima Ashkeboussi, vice president of government relations and communications at GLE.

While each one of its units is more complex and expensive than a centrifuge, far fewer are needed to do the same work. A similar centrifugation plant would have many thousands of centrifuges working together, but a full-scale plant using GLE’s laser enrichment process would have fewer than a thousand of its units, says Stephen Long, the company’s CEO. Up-front investment should be smaller, and operating costs are also expected to be lower, partly because the process uses less energy than centrifuges, Long says.

GLE has a testing facility in Wilmington, North Carolina. In fall 2025, the company completed a demonstration pilot, processing several hundred kilograms of uranium. It decommissioned that system and is currently putting together a new demonstration at the North Carolina plant, which would show how the technology works at commercial scale.

The company also applied for a license with the US Nuclear Regulatory Commission for its proposed facility in Paducah. The final safety evaluation should be finished in November, and the final approval should come in 2027, Long says. The plan is to start processing material at the plant by 2030.

In the long run, there’s plenty of uranium on the planet to keep reactors running for decades. But as interest in nuclear power grows and the geopolitics of fuel shift, there could be short-term gaps or price spikes that alternative sources could help smooth out.

Laser enrichment plants could turn out to be cheaper than existing technologies, says Stephen Greene, a senior fellow at the Nuclear Innovation Alliance. But as with most new technologies, “you don’t really know until you try to build one.”

STAT+: Sarepta names Michael Severino, former Tessera CEO, as new chief executive

Sarepta Therapeutics, the bruised maker of Duchenne muscular dystrophy treatments, on Monday named Michael Severino, formerly the CEO of Tessera Therapeutics, as its new chief executive, starting Tuesday. 

Severino is replacing Doug Ingram, who announced earlier this year that he planned to retire after leading the biotech for nearly a decade. During his tenure, the company got three Duchenne treatments approved, but has faced ongoing questions about how effective they are and run into regulatory scrutiny over the safety of its gene therapy. 

The company’s share price has tanked from where it was at the beginning of 2025.

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STAT+: Pharmalittle: We’re reading about a U.S. peptide panel’s votes, Amgen scuffling with the FDA, and more

Good morning, everyone, and welcome to another working week. We hope the weekend respite was relaxing and invigorating because that oh-too-familiar routine of meetings, deadlines, and the like has returned with a vengeance. You knew this would happen, yes? To cope, we are relying, as always, on a cuppa stimulation. Our choice today is Earl Grey, an old standby. Feel free to join us. Remember, no prescription is required. Meanwhile, here are some items of interest to help you on your journey today, which we hope will be productive and meaningful. Best of luck and, of course, do keep in touch. …

A U.S. Food and Drug Administration advisory panel on Friday recommended that compounding pharmacies be allowed to manufacture the peptides epitalon and semax, but narrowly voted to recommend against manufacturing emideltide, STAT writes. The votes, which followed the panel’s decision on Thursday to recommend allowing pharmacies to make four other peptides, bring U.S. Health and Human Services secretary Robert F. Kennedy Jr. one step closer to his mission of making these unapproved compounds more available for Americans. Peptides have become increasingly popular in the U.S., driven by endorsements from social media influencers.

Amgen submitted new evidence to the FDA ​as it seeks a hearing to challenge the proposed ‌withdrawal of its rare disease drug Tavneos from the U.S. market, Reuters says. In April, the agency proposed withdrawing the drug, which treats a rare autoimmune ​disease that damages blood vessels, citing a lack of proven effectiveness ​and false statements in its original marketing application. Amgen strongly disagrees with the FDA and noted its submission includes more than 70 real-world studies involving over ​2,200 patients supporting the drug’s effectiveness and safety.

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STAT+: Ahead of hearing, FDA questions the efficacy of Capricor’s Duchenne drug

The Food and Drug Administration said Monday that Capricor Therapeutics’ stem cell treatment for Duchenne muscular dystrophy did not meet the objectives of a Phase 3 trial — contrary to the company’s claims last year.

Capricor said in December that the drug, known as deramiocel, met both the primary and secondary endpoints in a large, randomized study. It was a striking result in a fatal, childhood disease that has proven stubbornly difficult to treat, despite immense advances in genetic medicine. 

The data were also notable for coming primarily in teenagers and young men who have already lost the ability to walk, a population with few options. The drug appeared to both preserve their upper-arm function and stave off the heart failure most patients eventually experience, Capricor had said. 

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Research progress on the α-synuclein-lysosome axis in Parkinson’s disease: molecular mechanisms of protein aggregation, autophagy dysfunction, and therapeutic targeting

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized pathologically by the loss of dopaminergic neurons in the substantia nigra and the formation of Lewy bodies, which predominantly consist of misfolded α-synuclein (α-Syn) aggregates. Recent advances have highlighted the critical role of the interplay between α-Syn and lysosomal function, termed the α-Syn-lysosome axis, as a central mechanism underlying PD pathogenesis. This review systematically summarizes the molecular mechanisms driving α-Syn aggregation and the lysosomal dysfunction contributing to impaired autophagy-lysosome pathway (ALP) activity. We further discuss emerging therapeutic strategies targeting this axis to restore lysosomal function and mitigate α-Syn toxicity. By integrating the latest findings from molecular biology, cell biology, and preclinical studies, this article aims to elucidate the complex regulatory network of the α-Syn-lysosome axis and provide a theoretical foundation for the development of novel therapeutic interventions for PD.

Exploring atypical spatial-functional coupling in adolescent autism spectrum disorder: insights from neurodevelopment and transcriptomic architecture

Autism Spectrum Disorder (ASD) is associated with atypical large-scale brain network organization, yet how spatial-functional dependencies relate to clinical features and molecular reference maps remains incompletely understood. To quantify spatial functional heterogeneity (Sill) and coherence persistence (Range), we analyzed resting-state fMRI data from 162 ASD and 175 TD adolescents, all aged 12–18. Compared with TD, adolescents with ASD exhibited significantly increased Sill within higher-order association networks, including the left Language and right Posterior Multimodal networks, whereas no group differences in Range survived multiple-comparison correction. Within the ASD group, elevated Sill was selectively associated with greater social-affective symptom severity but not restricted and repetitive behaviors. To explore potential biological correlates, we integrated cortical gene expression reference data and identified transcriptomic patterns associated with regional Sill differences. These genes showed enrichment for synaptic signaling, mitochondrial processes, and glial-related functions, highlighting multiscale correspondence between spatial-functional organization and molecular reference maps. Together, these results demonstrate statistical associations among altered spatial-functional properties, clinical severity, and transcriptomic profiles related to synaptic signaling, mitochondrial processes, and glial-related functions in ASD, providing a complementary spatial perspective on large-scale functional organization.