Nabla, the Paris-based maker of ambient scribes used to automate clinical documentation, says it’s playing the long game. On Tuesday the company announced a new leader and reiterated its commitment to fundamentally new AI technology it believes can help it beat the competition.
The company, which last raised a $70 million Series B last summer, announced a new CEO, Brian Manning, who has served as chief revenue officer at care coordination software company PatientPing and its acquirer, Bamboo Health, before taking over as Bamboo’s president. The shift “marks the next phase of Nabla’s go-to-market strategy,” the company wrote in a press release.
“We really haven’t built our brand in the United States. We really haven’t accelerated our go-to-market in line with what others are doing. And as we look towards 2027, that’s something we’re absolutely going to be doing,” Manning said in an interview with STAT. Chief operating officer Delphine Groll said that 100% of the company’s revenue is from the U.S.
Novo Nordisk on Tuesday sued Eli Lilly for allegedly running a “deceptive” ad campaign that uses “outdated clinical trials” to make its obesity and diabetes drugs appear more effective, marking a new phase in one of the pharmaceutical industry’s most intense rivalries.
In a lawsuit filed in federal court in New Jersey, Novo argued that Lilly has been comparing the highest doses of its weight loss drug Zepbound and diabetes drug Mounjaro with lower doses of Novo’s Wegovy and Ozempic.
“What has brought us to this moment is what we now see as a nationwide pattern, by Lilly, of deceptive advertising. They are intentionally confusing consumers,” John Kuckelman, Novo’s general counsel and senior vice president, said in an interview.
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Good morning. Sending thoughts and prayers to all the bagged salad girlies out there. For news, don’t miss Sarah Todd’s great story on THC beverages, which was spurred by a colleague’s rant in one of our regular team meetings.
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials.
Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI.
Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions.
As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.
Performance first
Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding.
Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions.
These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible.
For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly.
Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives.
At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs.
For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure.
Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability.
A new definition of what performance means
While performance remains the first priority, the way performance is defined is changing.
In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly.
Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals.
To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it.
This approach reflects a broader reality across the industry.
New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology.
Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset.
Accelerating the pace of discovery
As the performance bar rises, the way we innovate must evolve with it.
Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery.
AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges.
At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers.
AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy.
The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers.
Progress is earned
The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible.
Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place.
For materials companies, that remains both the challenge and the opportunity.
This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.
Two children with an ultra-rare epilepsy linked syndrome called SCN2A-developmental and epileptic encephalopathy (DEE) have been treated with personalized antisense oligonucleotide (ASO) therapies for their specific genetic mutations.
Both children, 9 and 14-year old boys, experienced significant improvements from the therapy. They both had less seizures and could cut back or stop some of the anti-seizure medications. They also achieved improvements in language and motor skills, with the older patient walking independently for the first time after being treated.
SCN2A‑DEE is a severe early‑onset epilepsy syndrome caused by pathogenic variants in the SCN2A gene, which encodes a voltage‑gated sodium channel in excitatory neurons. These variants typically lead to DEE with difficult‑to‑control seizures and profound neurodevelopmental impairment and impact around 16,000 people in the U.S.
Like many rare diseases, children with SCN2A‑DEE often have different mutations, albeit in the same gene. “The therapy is deliberately designed to target the individual’s genetic diagnosis,” said principal investigator Olivia Kim‑McManus, MD, associate professor of neurosciences at UC San Diego School of Medicine and director of the Rady Precision Therapeutics Neuro-Interventional Program at Rady Children’s Hospital San Diego, in a press statement. “The ASO modifies genetic expression and what proteins are expressed.”
This study, which is published in Nature Medicine, involved investigation, design, and treatment of the two boys in parallel. To begin with, the researchers designed allele‑selective ASOs targeting heterozygous intronic SNPs in SCN2A to suppress the mutant transcript while sparing the wild-type copy.
They then ran two parallel first‑in‑human N-of-1 trials for the two boys with individualized dosing. The primary endpoints were seizure frequency and neurodevelopment, plus phenotype‑tailored measures like impact on autism-like symptoms, movement problems, and gastrointestinal function.
Notably, the team scanned 19 infants with related SCN2A disorders and found that three of them could be eligible for treatment with the same ASO as the one given to the 14 year old boy in the future, as it is designed to treat a number of different mutations on the SCN2A gene.
Both boys had fewer seizures on the customized drug. The younger child had an estimated 26% reduction and could come off phenytoin treatment without losing overall control, while the older child’s seizures fell by about 90%, with longer seizure‑free stretches and less need for rescue medication.
Families and clinicians also saw improvements in motor function, abnormal movements, communication and gastrointestinal symptoms, and no serious safety signals emerged from the study.
“We’ve seen changes across the board, showing that targeting the root genetic cause can produce measurable improvement,” said Kim‑McManus.
One key improvement was that the older child began to walk on his own for the first time. “Since then, he’s been walking independently,” said Kim-McManus. “When we really think about precision therapy in a personalized way, you can’t get more personalized than that.”
The researchers are now hoping to use a similar approach to treat more children with SCN2A-related disorders as well as other monogenic conditions.
Covid fatigue is real, but policymakers and policy influencers need to wake up to something important: Post-pandemic polarization threatens to lock our memories into attractive, untrue conclusions that will leave us unprotected next time.
When Covid-19 first arrived, there was little historical experience to guide response. After watching catastrophic results and stress on the health care systems in China, Italy, and Iran, American states rapidly invoked many measures simultaneously to combat a virus with proven destructive power: masks, social distancing, and school and business closures. They turned to the 1918 flu pandemic to understand the importance of rapid and society-wide response.
WASHINGTON — Biotech companies are lobbying the Trump administration to exclude treatments for rare diseases from programs that lower brand drug prices in Medicare.
The Rare Disease Company Coalition met last week with the White House Office of Management and Budget to discuss two pilot programs that are part of President Trump’s plan to get drugmakers to lower prices in the United States to levels charged in other rich countries, a policy generally referred to as most-favored nation.
