Radically different
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Nature Medicine, Published online: 24 April 2026; doi:10.1038/s41591-026-04354-1
In a biomarker-driven trial evaluating radiotherapy with erlotinib, everolimus or dasatinib in patients with newly diagnosed diffuse intrinsic pontine glioma, the primary endpoint of overall survival was not met, but features associated with long-term survival were defined, and everolimus emerged as a potential candidate for further testing.
Glucagon-like peptide-1 was discovered as an insulinotropic peptide from the gut during a search for candidates for the incretin effect. It turned out to also inhibit glucagon secretion and is now considered an important regulator of glucose metabolism. In further investigations of its physiological effects, it also inhibited gastrointestinal secretion and motility and inhibited appetite and food intake. Because of these effects, it was eventually demonstrated to be able to improve glucose control and beta cell function in T2DM patients and was even associated with weight loss.
Nature Neuroscience, Published online: 24 April 2026; doi:10.1038/s41593-026-02247-7
By studying 23 neurodevelopmental disorder genes across model systems and brain cell types, the authors uncovered shared downstream effects that converge on synaptic biology, epigenetic regulation and mitochondrial function.
Japanese pharmaceutical companies are engaging CDMOs earlier in the development cycle, as increasing complexity in peptide programs places greater strain on in-house capabilities, according to officials at Neuland Laboratories, which is attending CPHI Japan this week. The company says it has seen a notable shift in demand over the past 12–24 months, with more early-stage programs seeking external support.
This trend is being driven in part by growing activity from venture-backed biotech companies and spinouts emerging from large pharmaceutical R&D organizations, reports a Neuland spokesperson, who adds that as these programs advance into clinical development, demand for specialized CDMO capabilities is increasing.
Neuland has observed a rise in peptide-related engagements from Japanese companies, particularly at the preclinical and early clinical stages, where technical requirements are more demanding, notes Sharadsrikar Kotturi, PhD, CSO at Hyderabad, India-based Neuland Labs.
Peptide development presents several challenges compared with traditional small molecules, explains Kotturi. Analytical complexity remains a key issue, with structural characteristics making characterization, impurity detection, and purity assessment more difficult, he continues. Scaleup is also constrained by the availability and quality of protected amino acids, which can affect manufacturing timelines, cost, and overall success rates.
Regulatory expectations further add to the burden, points out Kotturi. Demonstrating purity, consistency, and process control to authorities such as Japan’s Pharmaceuticals and Medical Devices Agency requires extensive data, while shifting requirements introduce additional hurdles during development and approval. Simultaneously, pricing and regulatory pressures in Japan are increasing the operational load on drug developers, he states. Frequent drug price revisions are pushing companies to improve cost efficiency, reinforcing the case for outsourcing.
“The bottleneck isn’t discovery anymore. It’s execution,” says Kotturi. “In peptides, programs are running into challenges around analytical complexity, scaleup, and the availability of key raw materials such as protected amino acids.”
The post Japanese Pharma Companies Turning to CDMOs Earlier in Product Life Cycle appeared first on GEN – Genetic Engineering and Biotechnology News.
I don’t need to tell you that AI is everywhere.
Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients?
We don’t yet have a good answer.
That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto, argue in a paper published in the journal Nature Medicine this week.
Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies, they have also begun rapidly deploying them.
The problem is that many providers aren’t rigorously assessing how well they actually work.
Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients, then transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers.
A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. Early studies support these anecdotes and suggest that the tools can reduce clinician burnout.
That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.”
The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient.
But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest X-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately: What will this mean for those patients?
The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers.
Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says.
In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias.
The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens.
“I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.”
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Since I was 7, my goal has been to become a doctor. But life had other plans. I grew up in a blue-collar family in Levittown, N.Y., in the 1950s and ’60s, so it often felt like the world ended in Jersey. When I landed in Lansing, Mich., to attend Michigan State, I expected the Rocky Mountains to be visible. I ended up getting a degree in nursing, but I always had another goal: to become an M.D.
This year, at the age of nearly 73, my dream will finally come true. Soon after, I will start my residency in family medicine. My perspective on medical school and medicine is unique not only because I attended late in life, but because it came after more than 40 years as a nurse practitioner.
President Trump recently signed an executive order which aims to increase access to psychedelic drug treatments. He was joined at the signing by podcaster Joe Rogan, who said he’ ha’d messaged the president about research on the psychedelic ibogaine.
In this week’s STATus Report, host Alex Hogan chats with STAT Washington correspondent Daniel Payne about what the executive order does and doesn’t do. Hogan also looks at why ibogaine, and psychedelic drugs more broadly, are increasingly being taken seriously for stubbornly hard-to-treat conditions like addiction, depression, and PTSD.
The past four months have been a whirlwind for Pittsburgh’s journalism landscape. On Jan. 7, the Pittsburgh Post-Gazette, Western Pennsylvania’s largest news organization, announced it would cease publication on May 3 after nearly 240 years. Then, on April 14, just over two weeks before that closure date, the Baltimore-based Venetoulis Institute for Local Journalism said it would acquire the paper’s assets and continue publication.
Like many Pittsburghers, I experienced the emotional rollercoaster of anger, disappointment, hope, and relief tied to these announcements. I grew up in the Pittsburgh area, where I vividly remember running barefoot down my driveway as a child to grab the Post-Gazette. Years later, I interned there as a health and science reporter and have since contributed as a freelancer.
Should academic journals begin to second guess guest editors?
That question gained new urgency last week when the British Medical Journal’s publishing group retracted nearly its entire guest-edited special edition of the Journal of Medical Genetics, dedicated to cancer immunotherapies. In the retraction note, the journal writes that it was, in part, because of “compromised peer review in almost all articles.” The notice garnered attention for its scope, but also because it exemplified larger concerns that research integrity advocates have with guest-edited editions, which are also called special issues in some journals.
Psychological Review, Vol 133(3), Apr 2026, 515-533; doi:10.1037/rev0000569
Manipulation plays a critical role in working memory, wherein understanding how items are represented during manipulation is a fundamental question. Previous studies on manipulation have primarily assumed independent representations by default (independent hypothesis). Here, we propose the ensemble hypothesis to challenge this conventional notion, suggesting that items are represented as ensembles undergoing updating during manipulation. To test these hypotheses, we focused on working memory updating in accordance with new information by conducting three delayed-estimation tasks under addition, removal, and replacement scenarios (Study 1). A critical manipulation involved systematically manipulating the mean orientation of all memory stimuli, either increasing (clockwise) or decreasing (counterclockwise) after the updating process. Following the independent hypothesis, memory errors would be similar under both conditions. Conversely, considering the biasing effect of the ensemble on individual representations, the ensemble hypothesis predicts that memories of individual items would be updated, aligning with the ensemble’s change direction. Namely, memory errors would be more positive in the increase-mean condition compared to the decrease-mean condition. Our results supported the ensemble hypothesis. Furthermore, to investigate the mechanisms underlying ensemble computations in updating scenarios, we conducted three ensemble tasks (Study 2) with similar designs to Study 1 and developed a computational model to quantify the contributions of each memory item. The results consistently demonstrated that addition involved complete updating, while removal led to incomplete updating. Across these three research parts, we propose that items are represented as dynamic ensembles during working memory updating processes. Furthermore, we elucidate the computational principles underlying ensembles throughout this process. (PsycInfo Database Record (c) 2026 APA, all rights reserved)