Biogen and Denali Therapeutics said Thursday that their experimental therapy for Parkinson’s disease failed to slow the degenerative brain disorder in a randomized trial, dealing a substantial blow to a scientific approach that stoked excitement among advocates and academics.
In the study, 648 adults with Parkinson’s were randomized to receive either a placebo or a pill targeting a protein called LRRK2. In 2004, researchers discovered that mutations in the LRRK2 gene can cause a rare, inherited form of Parkinson’s. And in 2018, another group of scientists showed that blocking the protein might actually benefit all patients with the disease.
Thursday’s results are a significant setback to the latter idea.
BOSTON — Thomas Bartlett’s life changed in 2019 when he was diagnosed with late-onset myasthenia gravis (MG). The 15-year veteran of the Bio-IT World Conference and Expo took to the stage on Tuesday as one of seven speakers in the opening plenary session of this year’s conference, which focused on various aspects of rare disease research and treatment.
The session offered a poignant, but often uplifting, launch to the annual conference, which celebrated 25 years from its inception in 2002, when the event was produced by the IDG World Expo Group.
Speaking with Susan Ward, PhD, founder and executive director of the Collaborative Trajectory Analysis Project (cTAP), Bartlett described an active life and fulfilling work prior to his diagnosis, and some of the debilitating physical and emotional impact of his disease. “I have to plan everything. If I’m going to go out, I plan ahead of time where I’m going to go [and] the amount of time,” he said. “I have to plan recovery.” Bartlett’s MG has prevented him from working a full-time job, as he would need a full day of rest just to recover from each day. “The math doesn’t work.”
Bartlett is now an ambassador for MG Uniter Myasthenia Gravis, an online platform designed to support some 70,000 patients living with the disease in the United States alone. Though there are some treatments that alleviate disease symptoms, there currently is no cure. Bartlett’s disease was diagnosed early thanks to a quick-thinking primary care provider. A recurring theme in the session was the stark reality that many in the rare disease community wait many years for a diagnosis.
Of the estimated 7–8,000 rare genetic diseases, many not well understood. About one in 10 people in the U.S. “either has or will have a rare disease at some point,” Ward noted. In a conference of about 2,700 attendees, “there are going to be about 270 people on average who might have a rare disease. So the magnitude of the problem is huge, even though the numbers of people are quite small.”
Given the computational nature of the Bio-IT conference, Bartlett and Ward soon turned to data and the challenges with collecting and aggregating information from rare disease populations. Ward noted that centers of excellence in rare disease may have several patients but “no one center that has enough data for anybody to really learn much.” Aggregating data from multiple centers and across geographies is one possibility but due to the differences that exist between centers across states and countries, “you need a really rich and deep ontology” as well as “context for what those data mean,” she said.
And that’s not the only challenge. In many cases, rare diseases can present and progress differently in patients with the same condition. “Imagine you’re trying to design a clinical trial. You’ve got patients who are fluctuating [while] you’re really looking for patients who are slowly declining” and “patients who have intermittent remissions,” Ward noted. With that mix, “you’re going to have a very noisy trial.”
There are also other data sources that could provide value. Bartlett noted that wearable devices such as the Apple watch capture useful health-related data, but as it is not clinical data, physicians cannot use it. As someone with decades of tech experience—including a stint at Apple—Bartlett asked: “How do we change that?” How do we prove the patient’s experience with the data that we can collect, and work with companies and legislation” to “include real world data and evidence and compare that … with the clinical data and get a much broader picture.”
Bartlett’s closing comments focused on hope for people living with rare diseases. And with good reason given recent successes in development of gene therapies and other therapeutics. As a patient, “you need to have something that you can look forward to today,” he said. For now, MG is incurable but “we will find ways to get to that end game and ultimately have a cure or at least a high level of quality of life.”
Shortening the rare disease diagnostic journey
Sebastien Lefebvre, head of technology, data and AI at Aurelis Insights, has spent 10 years in the rare disease space in different capacities including developing platforms for digital decision support or “data-driven and AI-assisted support decisions.” Speaking with William Van Etten, PhD, co-founder, CEO and principal scientist, StarfleetBio, Lefebvre described Rare Answers, a clinical decision support platform for rare disease diagnostics that he worked on while at Alexion Pharmaceuticals. The platform was designed in collaboration with two children’s hospitals as well as a number of technology and data science companies.
Sebastien Lefebvre, head of technology, data and AI, Aurelis Insights (right) and William Van Etten, PhD, co-founder, CEO & Principal Scientist, StarfleetBio (left). [Uduak Thomas]
He also described a second project in 2022 with the Rare-X program that analyzed data from public databases of rare and inherited diseases, drugs, and genes. Lefebvre and his colleagues hoped to produce an accurate assessment of the total number of rare diseases. Following extensive data cleaning and normalization—now made much simpler with advances in AI and machine learning—they arrived at a figure closer to 12,000. Of that number, between 80–87 percent have a genetic basis and about 80 percent had at least three associated phenotypic descriptors. That kind of information provides a viable starting point for applying AI-assisted data-driven diagnostic approaches. This is important because, as Bartlett noted, many patients with rare diseases wait years for a diagnosis. “It all starts with a diagnosis,” Lefebvre said. “[If] you don’t known what you’ve got, how can you [treat it].”
Van Etten is focused on making individual genomes truly private. The emergence of commercial personal genomics companies created a data privacy problem. Customers pay for their genomes to be sequenced by a company that holds the data, reads it, and sends periodic reports. He has developed an app called DNAVault that lets people host their genomes on their smartphones, putting data control back into their hands.
Van Etten’s new company, StarfleetBio, is partnering with his former consulting firm, BioTeam, and the Hubbard Center for Genomic Studies at the University of New Hampshire, to provide sequencing services.
“It used to be that we needed to centralize all the human genome data because you need a lot of compute to perform the analysis, but it’s really not required anymore,” he said. “We decided to decentralize it, where your genome is on your phone, you can generate your own reports, and nobody has access to it but you.”
Each encrypted genome is only accessible with a key unique to the individual’s phone. This way, only they individual can download their data and read it. Some audience members clearly approved of Van Etten’s app, with shouts of “Bravo!” from the back of the hall. (The app was later named one of three “Best of Show” winners at this year’s meeting.)
Among the features in the app is a fun kinship feature, which lets two people determine if they are related by placing their phones in close proximity as if sharing a Wi-Fi password. Another feature dubbed “origins” lets people track their ancestry over thousands of years via their Y-chromosome or mitochondrial DNA. Van Etten was particularly moved by the kind of insights this feature revealed about human relationships. “We found that all humans are far more closely related than we thought,” he said. “We all really [came from] the same 5,000–10,000 people from 50,000–70,000 years ago.”
Another app feature screens for the 81 ACMG medically actionable genes to provide health reports, while a final feature lets people ask questions about their genome and get answers much the same way one might enter a question into Google or ChatGPT.
Tying this to rare diseases, Van Etten is working on ways for app users to opt into participating in relevant research studies and clinical trials. The idea is that users could “toggle a switch” that would let alert the relevant researchers and then answer questions to help gauge eligibility. Importantly, this would all be done without people having to share their primary information.
Learning from rare diseases to treat common conditions
Another plenary conversation took place between Morgan Cheatham, MD, partner, head of healthcare & life sciences, Breyer Capital, and Catherine Brownstein, PhD, manager of the Molecular Genomics Core Facility at Boston Children’s Hospital and scientific director of the Manton Center for Orphan Disease Research Gene Discovery Core.
Morgan Cheatham, MD, Partner, Head of Healthcare & Life Sciences, Breyer Capital (left), and Catherine Brownstein, PhD, Manager of the Molecular Genomics Core Facility at Boston Children’s Hospital and Scientific Director of the Manton Center for Orphan Disease Research Gene Discovery Core (right). [Uduak Thomas]
Brownstein and Cheatham use OpenAI’s generative AI to help diagnose patients who in some cases had been waiting decades for answers. Importantly, “this was a zero-shot model,” Cheatham noted. “We didn’t do any specialized training of GPT-3, we just deployed the existing models and we’re able to return answers to families who have been waiting for sometimes over a decade.”
He also acknowledged the contributions of people living with rare diseases to many major drug modalities including CAR Ts and RNA medicines. “Many of those modalities were actually validated” with “the help of rare patients who were willing to participate in trials that allowed us to show the efficacy, the safety, and the durability of these modalities.”
According to Brownstein, a deeper understanding of rare conditions often has implications for more common conditions. “As someone who spent six years studying hypophosphatemic rickets … it’s these extreme cases, these rare presentations of disorders where you don’t know the underlying etiology [that] inform the common diseases,” she said. Understand the biology behind hypophosphatemic rickets “has implications for bone density and osteoporosis that affects a ton of us in this room.”
Many opportunities were highlighted where some form of AI is already being used or could be applied. One company that Cheatham mentioned is applying AI to colonoscopies to characterize inflammation levels in the bowel in a standardized way with an eye towards connecting patients with ulcerative colitis and Crohn’s disease to relevant clinical trials. There are also opportunities in cardiology, neurology, pathology and more.
Giving more patients the right to try
In the closing conversation, Van Etten spoke with Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI). Livingston is at the forefront of efforts aimed at implementing policies in different states that allow patients with rare diseases to try treatments that may benefit before they have been approved.
The story of how Livingston, still in his 20s, got involved in healthcare policy is interesting. As a college senior during the Covid-19 lockdowns, “I started thinking about COVID as it relates to age [and] why … [I] would be pretty much completely unaffected by COVID and why my grandfather at 92 would most likely die,” he recalled. “It all comes back to aging, your immune response to these diseases and your immune response to chronic diseases overall.” That got him interested in the field of aging and longevity more broadly.
Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI) (left) and William Van Etten, PhD (right) in conversation at Bio-IT World 2026 [Uduak Thomas]
Livingston and his group have worked to pass laws in the state of Montana that extend eligibility under the Right to Try Act, a piece of federal legislation that lets people with terminal illnesses try therapeutics that may help them which are not yet fully approved. The issue with the Right To Try Act as it stands is that “the definition of who is eligible is very narrow” and restricted to people with months left to live “which made no sense to me” Livingston said. From his perspective, people just diagnosed with conditions like Alzheimer’s or Parkinson’s should also have the chance to access treatments which could potentially help them earlier in their journeys as those who are further along in their journeys.
Expanding the Right to Try provides a possible pathway to those treatments without requiring approval from the U.S. Food and Drug Administration, which may be years away.
Livingston and his team have been successful in expanding the law in Montana to cover people with age-related ailments as well as people with rare diseases, people recently diagnosed with terminal diseases, and people with disease that will eventually become terminal. Now he and his team are working on getting similar changes in place in New Hampshire. There are safeguards in place: the proposed treatment has to be prescribed by two physicians, pass through IRB review, and the therapy must have passed a Phase I testing. “What we’re trying to do is create a system that is safe enough to prevent as many tragedies as possible while also opening up access to as many people as possible,” Livingston said.
As an example of the benefit of changing the law, Livingston shared a story of a father whose son had died from a rare mitochondrial disease. The father has since had the genomes of his two other children sequenced, only to discover that they carry the same mitochondrial mutation. In this scenario, Montana’s model would allow the father in this instance to bypass the strict requirements of a drug trial and access treatments that could potentially help his children.
“Maybe it’s not as great in terms of a data collection standpoint for companies, but what we’re offering here [are] options for people that don’t have any other options.”
*Bio-IT World Conference & Expo, Boston; May 19-21, 2026.
In January 2012, Kodak, a name synonymous with analog photography, filed for Chapter 11 bankruptcy protection, an event caused in part by the company’s inability to anticipate and adapt to the digital revolution.
A similar fate might well have befallen its Japanese competitor, Fujifilm. But last year, the Japanese giant recorded record sales and record profits, priding itself on its ability to innovate and evolve.
Part of that evolution and commitment to growth was on full display this week in Madison, Wisconsin, as Fujifilm executives and state officials officially opened the new facility of FUJIFILM Cellular Dynamics, Inc. (FCDI), which houses nearly 200 employees with a view to developing tomorrow’s life-saving therapies.
“The collaboration between Wisconsin and Japan has been fantastic,” said Tomoyuki (Tom) Hasegawa, CEO of FCDI for the past four years. “We are making a global impact together.”
The work here is “incredible,” Hasegawa continued. “All our employees are excited!”
Among the dignitaries speaking prior to the official ribbon-cutting ceremony were Wisconsin Governor Tony Evers, a 74-year-old Democrat. “Cleaning out a drawer the other day,” Evers joked, “I found a box of Fujifilm. I’m not sure what to do with it!”
Having toured Fujifilm’s Japan headquarters in 2019, Evers called the opening “a full-circle moment” and “a true Wisconsin success story.” The state’s expertise in stem cell biology began more than 20 years ago with the pioneering research of James Thompson, PhD, who co-founded Cellular Dynamics in 2004. The company was acquired by Fujifilm in 2015.
The new facility will quadruple FCDI’s cell therapy research and manufacturing, which Evers said, “may hold the key to Parkinson’s disease, Alzheimer’s, cancers, autoimmune and liver disease and other serious health conditions.”
Also speaking was Toshihisa “Toshi” Iida, a 30-year Fujifilm veteran who currently serves as corporate vice president and general manager of Fujifilm’s Life Sciences Strategy Headquarters and Bio CDMO Division.
Fujifilm was founded in 1934, Iida said, with a continuous push to combine technology and innovation. “Our journey has not always been easy,” he said. In 2000, the company’s core film business accounted for 70 percent of the firm’s profits. The business did not just decline; “It was a collapse,” he said.
“We overcame this challenge. I am a living witness to this transformation story.”
While Fujifilm’s business has evolved and diversified, the company’s purpose pays tribute to its photographic roots: “Giving our world more smiles.” The company’s interest in human health dates back to its early years. Just two years after its founding, Fujifilm produced the first X-ray film. “Our technology must serve people and improve lives,” Iida said.
Over the past 15 years, Fujifilm has invested some $10 billion in health and biologics. The investment in Madison is just a part of that bigger program. “This site is not just a building, but a platform to support innovation,” Iida said. “Japan is the birthplace of iPS cells. This has the potential to change medicine.”
Field of dreams
Hasegawa has been with Fujifilm for his entire professional career of 28 years, joining the company fresh from completing a law degree at the University of Tokyo. The one thing he knew was that he did not want to pursue a law career.
It was the late 1990s and the dawn of the digital camera revolution. “The company’s transformation story sounded very interesting to me,” Hasegawa told me in an interview in the exhibit hall at the American Society of Gene and Cell Therapy in Boston. “Especially right after Windows 95 came out, those were very exciting days expanding the digital capability of the company globally.”
FUJIFILM Cellular Dynamics CEO Tom Hasegawa at his company’s booth at the American Society of Gene and Cell Therapy 2026 in Boston. [K. Davies]
Before long, the company set its sights on the U.S. market, and Hasegawa moved to New York to lead that effort, before returning to Tokyo in 2011 and assuming direction of the global marketing team. Three years later, he moved into business development and was put in charge of corporate planning and cultural affairs.
Hasegawa recalls reading about the induced pluripotent stem cells (iPSCs) by accident. In 2012, the Japanese scientist Shinya Yamanaka, PhD, famously won the Nobel Prize in Physiology or Medicine for the identification of factors crucial in the reprogramming of mature cells into pluripotent stem cells.
Following the acquisition of Cellular Dynamics, Fujifilm “asked me to be in charge” of this fledgling program in regenerative medicine, Hasegawa said. “This is a very well-known story in Japan because [Yamanaka] won the Nobel Prize and it is a very interesting technique. He’s a superstar! That’s why Fujifilm believes that the cell and gene therapy [CGT] field could be our next field of dreams.”
Thompson built a foundation in iPS cells in Cellular Dynamics and “ introduced lots of new [cell types] derived from iPS cells. Cellular Dynamics became a global leader for iPS-related products,” Hasegawa said. “Fujifilm’s concept was very interesting—we wanted to explore the synergy of imaging, analysis, and digital technologies to support the pharma industry. That’s what happened in 2015.”
Hasegawa says he has “a big dream” for the company’s growth. “One way is to deliver our iPS cell products, which include cardiomyocyte cells and neuron cells. We have 40 types of cells for toxicity and efficacy testing for pharma companies to do their R&D. We are a leading supplier of these iPS cells, which have high quality and also the same donor cell for multiple neurons to enable isogenic research.”
At one time, Hasegawa concedes, “we wanted to be a king of pharma, but we switched our strategy.” The company invested heavily in its services to support drug discovery through commercialization, including service lines across contract development and manufacturing (CDMO) and small-molecule drug development. In 2017, Fujifilm acquired reagent company Wako Pure Chemical, which has an HQ in Japan and a subsidiary in Richmond, VA.
Another strand to Fujifilm’s bow is as a provider of materials including reagent kits. “As you remember [in cinema], film is not necessarily the hero—cameras and cameramen are the heroes,” Hasegawa said. “We support [R&D], so that kind of mindset is very beneficial.”
Another thriving business is cell therapy utilizing iPSC technology. Hasegawa points to another subsidiary that provides cell therapy CDMO services in California. “We have an end-to-end service for cell therapy,” he said. In the cell therapy space, he is not looking for additional acquisitions, “because we shifted our strategy.” But for the supporting industry, “if there are good opportunities, we can of course have a discussion about it.”
Fujifilm says its $200-million investment in the new FCDI facility will “help secure America’s supply chain for biotech and regenerative medicine.” The new facility will feature state-of-the-art resources, including a Center of Excellence for genome editing and laboratories for cell culture manufacturing and process development. The space will quadruple capacity for R&D and manufacturing and enhance the company’s capabilities in drug discovery support and process development.
The company believes the investment will help Fujifilm keep pace with the rapidly expanding cell therapy market—and put a smile on the faces of patients and company executives alike.
Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. tests were performed to examine stakeholder differences in desired AI features. Results: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, (n=205)=34.78, <.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.
AI is increasingly being used to help determine breast cancer risk, with two studies showing its value for primary prevention and in women of diverse ancestry.
The first study, in Science Translational Medicine, revealed that AI could outperform standard clinical risk tools at identifying women of European ancestry who were at greatest risk of developing breast cancer within the next decade.
Because current AI-based models mostly predict breast cancer risk in the short term, Mikael Eriksson, PhD, from the Karolinska Institute, and fellow researchers created a model for invasive and in situ breast cancer that covered a 10-year period.
“Considering that a tumor can take five to 20 years to develop into a screen- or clinically detected cancer, a 10-year or lifetime risk projection time is reasonable,” they noted.
The researchers validated their image-derived AI-based 10-year risk model using digital mammograms from 8676 women in two cohorts from the U.S. and Sweden, of whom 1633 had breast cancer.
They compared their model with three other risk assessment tools: the Tyrer-Cuzick-v8, which uses personal and family history to determine the lifetime risk of breast cancer; the Breast Cancer Surveillance Consortium v3 tool, which estimates the risk of developing invasive risk cancer over five years; and the Mirai AI algorithm for predicting breast cancer risk.
The model calculated that the 10-year risk of breast cancer was 3.83% for the North-American group and 3.14% for the Scandinavian group, and it showed promise for predicting the risk of invasive tumors.
The AI model performed significantly better at predicting invasive breast cancer than the three comparator models both overall, in women aged 50 to 69 years and those with estrogen-receptor-positive breast cancers.
“An image-derived AI-based risk model developed for 10-year risk assessment for identifying individuals in mammographic screening who may benefit from primary prevention strategies identifies up to 40% of breast cancers to be at high risk at study entry in U.S. and Swedish validation case cohorts per clinical guidelines,” summarized Eriksson and co-workers.
The second study, in Science Advances, revealed how AI could be of value for women from more diverse backgrounds, and that it had value regardless of race and ethnicity.
The research was driven by the observation that conventional prediction models incorporating genetic and clinical factors including breast density underperform in women who do not have European ancestry.
Shu Jiang, PhD, from Washington University School of Medicine in St Louis, and co-workers set out to evaluate the generalizability of an AI-derived mammogram risk score (MRS), a summary of texture features which captures intrinsic breast-tissue characteristics that are the basis for cancer to develop.
To do this, they used data from two large North American cohorts that represented more than 226,000 racially diverse women undergoing routine breast screening with mammography.
This was then used to examine the generalizability of MRS with breast cancer risk across non-Hispanic white (NHW), non-Hispanic Black (NHB), East Asian, South Asian, and Indigenous women.
Jiang and team found that the MRS was a strong predictor of breast cancer risk independent of race or ethnicity, demonstrating its potential for broader clinical utility.
Across the two independent screening cohorts, the MRS increased with age in line with rising breast cancer risk. It was a strong and consistent predictor of breast cancer risk across race and ethnic groups as shown by the association study and comparison of distributions and demonstrated excellent calibration in all groups.
Its remained robust across full-field digital mammograms or synthetic two-dimensional digital breast tomosynthesis, although the limited numbers of images precluded an analysis of how performance varied across manufacturers.
“Overall, these results support that MRS is a powerful breast cancer risk predictor that does not depend on race or ethnicity, supporting its potential for broader clinical adoption and use in varied populations of women undergoing routine screening mammography to identify those at increased risk,” Jiang and coworkers concluded.
Jonathan D. Grinstein, PhD, North American Editor of Inside Precision Medicine, hosts a new series called Behind the Breakthroughs that features the people shaping the future of medicine. With each episode, Jonathan gives listeners access to his guests’ motivational tales and visions for this emerging, game-changing field.
Brain-computer interfaces have captured global attention in recent years, but most public discussion has focused on assistive technologies—systems designed to help patients control computers, prosthetics, or digital devices through thought alone. Carolina Aguilar, co-founder and CEO of INBRAIN Neuroelectronics, believes the larger opportunity lies elsewhere: using advanced neural interfaces not just to decode the brain but to treat disease directly. Built around graphene-based technology, INBRAIN is developing implantable systems capable of reading and writing neural signals with far greater precision than traditional metal-based devices.
Before founding INBRAIN, Aguilar spent 13 years at Medtronic, including a decade leading the company’s global neuromodulation business. During that time, she saw both the extraordinary potential of brain stimulation therapies and the limitations of incremental innovation inside large medical technology companies. Her background in neuroscience research, including early work studying the relationship between pesticides and Parkinson’s disease, shaped a long-term interest in circuit modulation and neurotherapeutics. That experience eventually converged with the emergence of graphene as a promising material for next-generation neural interfaces.
In this conversation, Aguilar discusses why INBRAIN chose to focus on therapeutic BCI applications rather than assistive computing, how graphene may enable higher-resolution neural decoding and stimulation, and why Parkinson’s disease became the company’s first major target. She also outlines INBRAIN’s broader vision for personalized neurotechnology, AI-driven therapies, and future applications ranging from epilepsy and memory restoration to bioelectronic treatments for cardiometabolic disease.
This interview has been edited for length and clarity.
IPM: You spent more than a decade at Medtronic before founding INBRAIN. What made you realize that incremental innovation was no longer enough, and why did graphene feel like the right breakthrough technology at the right time?
Aguilar: INBRAIN is a graphene-based brain-computer interface (BCI) company developing the most intelligent and adaptive interface between the neural system and AI to solve health for billions.
I started my career in consumer goods, but then I spent 13 years at Medtronic, where, for ten of those 13 years, I was leading the brain stimulation, or neuromodulation, business globally. I was always extremely impressed by what the company vision was and the number of disease areas and patients that we could help. However, I saw that every year we were innovating, but it was a bit incremental versus breakthrough. It’s normal for big companies to prioritize preserving shareholder value and revenue opportunities.
Investing in breakthroughs is harder; they usually just acquire those breakthroughs and then integrate them. And, of course, they revitalized their innovation. So I thought at that point that I could be one of those breakthrough innovators that could take a little bit more risk but then bring much bigger value into the field.
When the right opportunity presented itself to us, after the European Union put €1 billion into bringing graphene to market and after having gone through different speeds as pieces, we realized that it was the time to build INBRAIN and make that breakthrough a step instead of the incremental innovation of the past.
It’s interesting because I actually studied neuroscience at Virginia Tech, and at Virginia Tech, I had to define my thesis to study for my master’s degree. I picked up a combination of pesticides in the study. The study focused on the effects of pesticides and their combinations on brain chemistry in Parkinson’s disease. Right. So it was already quite oriented toward what we are solving today. And I got a grant award for the best vision on circuit modulation. So it was not about targeting the brain. It was about the circuit that was actually managing some of these dynamics of Parkinson’s disease.
At that early stage, I was already very attracted to the problem we are solving today, even though I did not know I would have a company dedicated to that field and to this innovation. However, it was always driving me in that direction. Medtronic was the company that turned that ambition into a real product and broad platform, which I also helped launch globally. So I guess it was always there. It was the seed that had been growing over time.
IPM: Medtronic exposed you to commercial, upstream product development, and engineering challenges. What did you learn that prepared you to build INBRAIN, and how did graphene change your engineering perspective?
Aguilar: It also evolves over time. So we start with a problem, such as pesticides in the development of Parkinson’s. Then I think I stepped into Medtronic as a solution to the problem that existed at that time. I was like, great. Now we have a problem. We have a solution. Let’s fix Parkinson’s disease.
At Medtronic, I was exposed to new experiences even while working in a commercial role, as I became a global director. I was exposed to the upstream. At Medtronic, there is a downstream process from which all employees receive their compensation. Then there’s the upstream piece, which is the product development and the planning of the next platforms. I was not in charge of that, but I was exposed to the process, the thinking, what is coming next, how we can make it happen, and where the constraints are. And that intrigued me a lot.
I discovered that I had a little bit of an engineering part inside of me that I never exploited. When the right opportunity came, I received a call from an investor friend of mine, who sometimes asked me to do some small due diligence. And he said, “Hey, there are some guys who have some graphene technology here that you might understand better than me. Do you want to come to the pitch?” I said, “Perfect.”
Then I saw that some of these constraints that we were dealing with in Medtronic in terms of miniaturization, higher-resolution interfaces, charge injection limits, and many of these engineering problems, partially, we could also solve by going graphene and going a different kind of electronics and a different kind of platform. When these consolidated and we created INBRAIN, I had to put a huge amount of effort into understanding semiconductors, microelectronics, mechanical configurations, and data architectures. So it’s been a huge learning journey for me.
IPM: INBRAIN appears to be building a platform strategy across multiple applications and devices. What are the product verticals and why does graphene offer capabilities that traditional neural interfaces cannot?
Aguilar: So it’s effectively a platform with three product verticals. When Morgan Stanley released the BCI industry report, it stated that the market was a $400 billion opportunity. We already knew that with one device, you could not capture all that immense market. So we were already working on a stepwise approach to the biggest opportunity. And this is what we have today on the table in the sense that we have a first product. The first product I can show you is a cortical interface. Starting with about 100 contacts, we can grow to a thousand. But in this case, it was not necessary.
It’s made of graphene. So it’s not about the number of contacts but the quality of what these contacts can decode. What is the precision of anatomy and the resolution of disease-related biomarkers that we can decode at high resolution in a way that that resolution is superior to a standard medical technology?
In some cases, standard platinum and iridium are used; in some cases, iridium oxide. We decided to create this first product for use for less than 30 days once it’s approved. It’s not yet approved, but we are getting closer, and it is the one that we actually took first-in-human. The results of those three reports are being generated using this device to demonstrate both the safety of graphene and its decoding superiority compared to metal technology. And, of course, within that first-in-human study, we also decoded speech at the phoneme level.
It was about creating the first step toward making graphene and consolidating advanced materials in clinical practice. So from there, the second product is actually an implantable platform that uses some of that configuration. So we use a cortical and subcortical interface to actually decode a circuit.
In our case, the microcircuit that is involved in Parkinson’s disease. And that is the second one. And the third product is actually the same platform, where instead of these two cortical and subcortical interfaces, we put in the central nervous system. We are truly connected to a vagus nerve interface that decodes the fibers within the vagus nerve, which go into the different modulations of the different organs of the body.
We go from reading the brain to reading the body. This sequence of product verticals opens up the immense possibility of a $400 billion opportunity. But even more important is the number of patients we could actually touch and improve therapeutically with such a platform.
IPM: Many BCI companies focus on assistive applications like computer and prosthetic control. INBRAIN appears to be therapeutic. Why did you choose direct disease treatment, and what makes it harder?
Aguilar: People say “invasive” or “noninvasive.” I call them implantable and non-implantable. And within the implantable, there might be different levels of invasiveness, but we were looking at the problem to solve, and especially coming from the field we come from and having experience for ten years, the use cases we saw are assistive BCI, meaning I transfer thought to action in the computer as solving a very important problem, but actually in a small population because, at the end, paraplegia and ALS. They are very important disease areas, but compared to other areas, there are smaller populations.
There were many people and companies, great companies, already aiming to solve that. So Neuralink and Synchron and Paradromics were always positioned there. And we thought, well, we have a similar approach, but maybe with a material that is much better suited to actually read and write bidirectionally, therapeutically. In assistive BCI, you do a ton of decoding. So, you read it and then transfer it to the computer. On the therapeutic side, where we are doing it, you have to read, write, and do that computing already within the implanted system.
It’s a much more complex architecture and a harder problem to solve. Now the benefit is solving the disease for as long as the system is active. And Parkinson’s was only the beginning. We are currently examining the validation of this program. We’re looking at memory restoration. We are looking at a set of disease areas on the cardiometabolic side that I cannot disclose without an NDA that we are developing with Merck KGaA. Those deals were unexplored or suboptimally explored by some of the low-resolution companies in the field.
Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer’s disease progression using only a single baseline MRI scan and basic demographic information. The approach, published in Nature Aging, could help make early Alzheimer’s assessment faster, more accessible, and less dependent on costly specialized testing.
Alzheimer’s diagnosis remains complex and resource-intensive
Alzheimer’s disease accounts for approximately 60% to 70% of dementia cases worldwide. Although structural brain changes and cognitive decline are hallmarks of the disease, accurately forecasting who will develop progressive impairment remains difficult.
Current diagnostic workflows often rely on multiple complementary techniques, including PET imaging, cerebrospinal fluid or blood biomarkers, genetic testing, and comprehensive neuropsychological assessments. While effective, these approaches can be expensive, time-consuming, and inaccessible in many healthcare settings.
MRI scans are among the most widely available clinical imaging tools for neurological assessment, but MRI data alone has historically struggled to capture the complexity and heterogeneity of Alzheimer’s disease progression when used in conventional AI frameworks.
To address this challenge, the UCSF team designed a multitask deep learning framework that combines domain-specific imaging knowledge with advanced machine learning methods to predict cognitive outcomes directly from structural MRI scans.
AI framework predicts cognition without invasive testing
Unlike many earlier Alzheimer’s prediction models, the new system does not require longitudinal imaging data, baseline cognitive testing, PET scans, or molecular biomarker analysis.
The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan. The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer’s diagnosis prediction, and estimation of both present and future cognitive performance.
A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions. According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.
Senior study author Ashish Raj, PhD, professor of radiology and biomedical imaging at UCSF, said the goal was to create a system that could be realistically implemented in routine clinical environments.
“Unlike previous approaches, our model does not require baseline cognitive assessment, specialized image pipelines, expensive PET scans, genetic analysis, or fluid proteomics, making it a fast, accurate, and easily implementable tool for most clinical settings,” Raj said in a statement.
Large imaging datasets improved robustness and generalizability
To train and validate the framework, the researchers used imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including MRI scans, demographic information, diagnoses, and cognitive assessments.
The team also incorporated MRI data from the Human Connectome Project Young Adult cohort, which contains scans from healthy younger adults with minimal age-related brain atrophy. According to the authors, exposing the model to healthy brain anatomy improved its ability to distinguish pathological neurodegeneration from normal aging.
An external validation cohort from the Dallas Lifespan Brain Study was additionally used to test the generalizability of the framework across independent datasets.
The researchers reported that the multitask framework outperformed existing AI methods, including standard transfer-learning approaches, in predicting clinically relevant outcomes. The model generated accurate predictions for Alzheimer’s diagnosis, tissue segmentation, current cognitive function, and future cognitive decline using only baseline MRI data.
The study also reported improvements in computational efficiency and processing speed compared with more complex MRI morphometry pipelines commonly used in neuroimaging research.
First author Daren Ma, MSc, a machine learning specialist in the Raj Lab at UCSF, said the framework could help clinicians identify at-risk patients earlier and streamline referrals for advanced neurological evaluation.
“We reported meaningful gains in speed and performance over other pipelines, which could prove valuable in developing a quick clinical prediction of cognitive impairment prior to referring the patient to a more advanced imaging lab and/or a full neuroradiology report,” Ma said.
Potential implications beyond Alzheimer’s disease
The researchers believe the framework could eventually be adapted for other neurodegenerative disorders characterized by structural brain changes and progressive cognitive decline.
Potential future applications include Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and Huntington’s disease. The ability to estimate cognitive impairment using minimal baseline data may also prove useful in community healthcare settings where access to specialist neuropsychological testing is limited.
In addition, the model may have implications for clinical trial design. Identifying likely disease progressors early could help reduce trial size requirements and improve patient selection for studies evaluating disease-modifying therapies.
“The ability to correctly predict progressors from non-progressors using only baseline data can dramatically reduce sample sizes and cost,” Raj said.
The authors emphasized, however, that further validation will be necessary before the model can be broadly implemented in routine clinical practice. Future iterations of the framework may incorporate additional clinical measurements where available, including longitudinal MRI imaging, PET scans, genetics, and blood or cerebrospinal fluid biomarkers.
The study highlights the growing role of AI-driven imaging analysis in neurology and suggests that clinically accessible tools such as MRI may eventually support earlier and more scalable prediction of Alzheimer’s disease progression.
IntroductionGrowing evidence suggests a mechanistic link between type 2 diabetes mellitus and Parkinson’s disease (PD), with insulin-degrading enzyme (IDE) implicated in both insulin and amyloid-β metabolism, as well as α-synuclein degradation. However, the role of IDE in PD pathogenesis remains insufficiently defined. This study aimed to investigate the association of IDE gene polymorphisms and serum IDE levels with sporadic PD in a Chinese Han population.MethodsFourteen single nucleotide polymorphisms (SNPs) within the IDE gene were genotyped in 463 patients with sporadic PD and 576 age- and sex-matched healthy controls (HCs). An independent cohort of 100 PD patients and 100 HCs was used to quantify serum IDE concentrations. Correlations between IDE levels and clinical features were assessed. Logistic regression was employed to identify independent factors associated with PD.ResultsAmong the examined SNPs, rs11187007 showed a nominal allelic association with PD (P = 0.046), which did not survive the Bonferroni correction. Serum IDE concentrations were significantly higher in PD patients than in HCs (P = 0.015). Elevated IDE levels were negatively correlated with Mini-Mental State Examination scores (R = –0.230, P = 0.027) and positively associated with more severe symptoms. Logistic regression indicated that elevated serum IDE levels were associated with PD.ConclusionOur findings highlight that elevated serum IDE correlates with PD, suggesting a role for IDE in neurodegeneration, warranting further mechanistic and longitudinal studies to evaluate its potential as a therapeutic target in PD.
Niemann-Pick type C1 (NPC1) disease is a neurodegenerative lysosomal storage disorder caused by loss-of-function mutations in the NPC1 gene. NPC1 deficit primarily disrupts lipid homeostasis and subsequently drives cellular degeneration through mechanisms involving impaired autophagy and mitophagy, mitochondrial dysfunction, and, recently demonstrated NAD depletion that links autophagy impairment to neuronal death. Emerging evidence also highlights the activation of innate immune signaling leading to neuroinflammation. In this review, we synthesize current mechanistic insights and describe how these molecular deficits are interconnected to drive neuronal death in NPC1 disease. We also discuss how these pathological processes parallel those observed in major age-related neurodegenerative pathologies such as Alzheimer’s and Parkinson’s disease. Finally, we highlight emerging therapeutic strategies that can potentially ameliorate these cellular deficits, offering avenues for mitigating neurodegeneration in NPC1 disease and other related neurodegenerative disorders.
Parkinson’s disease (PD) is a major neurodegenerative disorder characterized by progressive loss of dopaminergic neurons and accumulation of α-synuclein. Current treatments primarily focus on symptom alleviation, highlighting the necessity for identifying novel molecular therapeutic targets. RNA editing, as a post-transcriptional process that modifies RNA sequences without altering genomic DNA, is increasingly recognized as an important contributor to the neuronal development and synaptic regulation. Among known RNA editing types, ADAR-mediated adenine-to-inosine (A-to-I) editing is the predominant form in the brain. Accumulating evidence suggests that RNA editing patterns undergo significant alterations in PD patients. This review synthesizes current evidence within a three-layer framework: (1) evidence for RNA editing dysregulation in PD, emphasizing tissue-specific and context-dependent patterns; (2) downstream mechanistic pathways stratified by evidence strength; and (3) experimental models, translational applications, and limitations. A distinction between what is known versus what remains speculative is emphasized throughout. RNA editing changes in PD appear heterogeneous and context-dependent, with brain and peripheral blood showing distinct patterns. Whether editing changes represent disease drivers, compensatory responses, or downstream phenomena remains largely unresolved.
![An image showing Sebastien Lefebvre, head of technology, data and AI, Aurelis Insights (right) and William Van Etten, PhD, co-founder, CEO & Principal Scientist, StarfleetBio (left) in conversation at Bio-IT World 2026. [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Seb-Bill-300x233.jpg)
![Morgan Cheatham, MD, Partner, Head of Healthcare & Life Sciences, Breyer Capital (left), and Catherine Brownstein, PhD, Manager of the Molecular Genomics Core Facility at Boston Children's Hospital and Scientific Director of the Manton Center for Orphan Disease Research Gene Discovery Core (right). [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Cheatham-Bownstein-300x232.jpg)
![Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI) (left) and William Van Etten, PhD (right) in conversation at Bio-IT World 2026 [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Livingstone-Van-Etten-300x225.jpg)
