WASHINGTON — Steve Ubl is stepping down as CEO of the Pharmaceutical Research and Manufacturers of America after more than a decade leading the brand drug industry’s main trade group.
Ubl plans to depart by the end of the year and will remain in his position until a new leader is found, according to a PhRMA statement.
Ubl led the organization during tumultuous times that included the Covid-19 pandemic and aggressive political attacks on drug prices. Democrats passed a law directing Medicare to negotiate drug prices, and the Trump administration struck voluntary deals with individual drugmakers aimed at lowering U.S. prices to levels in other high-income countries.
The Chinese biotech CorrectSequence Therapeutics, also known as Correctseq, reports good results from a Phase I study of its technology involving editing a person’s hematopoietic stem cells to treat beta thalassemia.
The trial, published in Nature, included five patients with transfusion dependent beta thalassemia who were able to stop red blood cell transfusions, the standard treatment for the condition, after receiving the base-edited treatment CS-101. The participants continued to have good levels of hemoglobin with no serious side effects during follow-up.
Beta thalassemia is a rare inherited condition affecting around one in 100,000 people in the U.S. Mutations in the beta‑globin gene HBB reduce or stop production of the beta chains of hemoglobin, leading to chronic anemia that varies in its severity.
There are already several therapies on the market for beta thalassemia. The most common treatment is still regular blood transfusions to treat the anemia, but recently the genetic therapies Zynteglo, a lentiviral gene therapy developed by Bluebird Bio, and Casgevy, a CRISPR edited therapy developed by Vertex Pharmaceuticals and CRISPR Therapeutics were approved by the FDA.
Casgevy works by boosting fetal hemoglobin levels to treat the anemia seen in thalassemia patients. It uses CRISPR–Cas9 to cut both strands of DNA at the BCL11A enhancer site, which relies on error‑prone repair and can theoretically generate insertions, deletions, and larger rearrangements.
Correctseq is also aiming to raise fetal hemoglobin levels with CS-101, targeting the same site, but is only changing individual bases without making a full cut, which should reduce risks linked to double‑strand breaks, such as large deletions or chromosomal translocations.
In this study, CS-101 was given to five patients with beta thalassemia, previously treated with blood transfusions. The process involves extracting their stem cells, reactivating fetal hemoglobin production using base editing, giving the patients chemotherapy to clear existing stem cells and make way for the newly edited population, and finally injecting the patients with the edited stem cells.
All five patients were able to stop red blood cell transfusions and had maintained good levels of hemoglobin at three months. These levels stayed at a similar level through a median follow up period of 23 months. No deaths or reported cancers due to the chemotherapy treatment were observed and the safety profile so far is acceptable.
Although these results are promising, this trial is just a small initial study and further work is needed to confirm safety and efficacy of CS-101.“The planned Phase II/III trial will be crucial for evaluating a larger and more genetically diverse patient population across multiple centers,” write the authors.
“Extended follow-up will be required to enable comprehensive analyses of chimerism and clonality, which will facilitate more definitive assessment of long-term safety, engraftment dynamics and clinical benefit.”
One Correctseq’s main competitors is U.S.-based Beam Therapeutics, which is developing a similar base edited treatment. Beam is behind Correctseq in developing its edited therapy for beta thalassemia, but ahead with its therapy for sickle cell disease, something Correctseq are also targeting using a similar pathway.
The Chinese biotech industry is currently on an upward trajectory. Correctseq is one of many Chinese biotech companies currently working to produce competitors for gene therapies like Casgevy and Zynteglo at a more affordable price than those seen in the U.S.
According to Isaac Rose-Berman, a fellow at the American Institute for Boys and Men, the skyrocketing popularity of sports betting is a burgeoning public health crisis, especially for young men.
As we continued with our medical training, we grew used to the idea that high-quality care inevitably produces high waste. But reading several published articles on the resource efficiency of hospitals in India forced us to question that assumption. How were they providing thousands of surgeries a day with a fraction of the waste — and no compromise in safety?
A federal judge Tuesday refused to block filling prescriptions for the abortion pill mifepristone by mail across the U.S. — at least for now — in a setback to Louisiana’s effort to stifle groups that send it into states where abortion is banned.
U.S. District Judge David Joseph, who sits in Lafayette, Louisiana, ruled against Louisiana Attorney General Liz Murrill, who asked that U.S. Food and Drug Administration rules that allow mifepristone to be dispensed through the mail be paused while a challenge to those 2023 regulations moves through the courts.
As cancer care becomes data-driven, artificial intelligence (AI) will play an increasingly central role across the treatment continuum, from biomarker identification and drug development to clinical trial recruitment and diagnostics. In this corner of healthcare, the ability of AI to interpret and annotate tumor sample slides that have been digitized is taking center stage. While the promise is great, and AI interpretation is already influencing some clinical care, it has not yet reached critical mass.
“There’s something like a billion slides created every year for diagnostic purposes, and today most of those, about 85%, are still read by a pathologist with a microscope on physical glass slides,” said David West, CEO and co-founder of digital pathology company Proscia. In practice, that means pathologists manually examine slides, identify cancer, grade tumors, and dictate reports in a traditional approach to diagnosing cancer that has seen little change in decades.
Mohamed Omar, MD Associate Professor Cedars-Sinai Medical Center
But that foundation is now shifting. Advances in slide scanning, cloud storage, and AI are turning digital pathology images into data that can be analyzed at scale. At Memorial Sloan Kettering Cancer Center, large archives of digitized slides helped launch Paige AI, one of the earliest companies to train deep learning systems on pathology images linked to clinical and genomic outcomes. This yielded the first U.S. Food and Drug Administration (FDA)-approved diagnostic using AI and digital pathology: Paige Prostate Detect. The company, which was acquired last year by AI-enabled precision medicine company Tempus, now combines Paige’s digital pathology-based AI with Tempus’s broad genomic sequencing data platform.
Researchers in the field say the implications of AI in digital pathology extend beyond image analysis. Mohamed Omar, MD, an associate professor of computational biology at Cedars-Sinai Medical Center, Los Angeles, noted that large language models can help clinicians navigate a research landscape that produces “hundreds of papers every single day” to inform ongoing cancer research. Multimodal AI tools promise to unlock even more insights from digital pathology data by combining it with genomic, radiomic, and clinical data to build powerful new models of both common and rare cancers for diagnosis, drug development, and clinical trial enrollment.
Razik Yousfi SVP and GM, Tempus
While adoption is in its early stages, the advent of faster and less expensive scanners is bringing digital pathology within reach of both regional and rural hospitals. Razik Yousfi, senior vice president and general manager of AI products at Tempus, and a co-founder of Paige, predicts that within the next 10 years, the majority of pathology workflows will be digital. The ultimate goal of the application of AI here is not to replace human pathologists, but to empower them with a capable assistant while spreading adoption beyond major medical centers.
Building the foundations
As the field of applying AI to digital pathology progresses, it needs to build the groundwork for a wider range of potential applications that could address rare cancers and other areas without an abundance of data. One such project is called Atlas, a collaboration between researchers in Korea, Germany, and the United States to build a foundation model trained using 1.2 million histopathology whole-slide images from 490,000 cases sourced from the Mayo Clinic and Charité – Universitätsmedizin Berlin.
Foundation models like Atlas allow large-scale pre-training of data to develop numerical representations called embeddings that capture both the structural and contextual features of slides in the dataset. Atlas incorporates a diversity of diseases, staining types, and scanners, and uses multiple image magnifications during training. This broad approach confers power and utility. It allows the digitized representations of the histology to be adapted, queried, or fine-tuned to very specific downstream tasks using much less data than would be needed to build a one-off model.
As such, a foundation model provides a reusable digitized computational backbone that can be tapped across a wide range of uses, like tumor classification, detection of morphologic structures, biomarker quantification, and outcome prediction. In short, foundational models make the process of querying digital pathology images more efficient compared with past approaches.
Andrew P. Norgan, MD, PhD CMO, Mayo Clinic
“In the case of pathology, the successful AI models developed using ‘conventional’ neural network approaches before the advent of FMs (foundation models) typically required huge amounts of training data to achieve high performance and generalizability—the ability to work across datasets distinct from the training data,” said lead Atlas researcher Andrew P. Norgan, MD, PhD, CMO of Mayo Clinic Digital Pathology and assistant professor of laboratory medicine and pathology. “We think of FMs as [an] enabler that allows model development in pathology … to move from artisanal or craft processes to more scalable and reproducible processes that should allow for the rapid development of high-quality models to address problems in pathology.”
At Paige AI, the company’s early work resulted in the first FDA-approved AI diagnostic, Paige Prostate Detect. Its algorithm was built using a technique called multiple instance learning instead of traditional supervised neural network techniques that require detailed human annotation of slides, a time-consuming and expensive method that could expose the learning to human error. The difference between the two methods is that traditional neural networks expose AI to a slide with cancer and tell it that there is cancer present. In multiple instance learning, the model is shown unannotated slides and is tasked with finding the cancer.
Even this approach, however, required a very large dataset. It became apparent to company leaders that the heavy lifting required to get Paige Prostate Detect to work wasn’t scalable.
“We had kind of cracked this recipe,” said Yousfi. “We know how to use a lot of GPU (graphics processing unit) compute, and if we get a ton of data and a lot of compute, we can build anything. But GPU infrastructure is very expensive, and it takes a lot of time to train a very large system.”
Perhaps the most important factor moving Paige away from this model is that it will not work when there is only a small amount of data available. This blocks the ability to train AI to recognize rare cancers for which sample counts are low. The company needed a different approach.
“We had this idea [for] a new system that was basically trained on all of the images we had access to, independent of the organ and indication and tissue and task,” Yousfi said. “Back then, we didn’t know what that thing was called. But ultimately, that became what everyone is calling today a foundation model.”
Originally trained on 200,000 slides, Paige’s new model now includes 3.5 million images and roughly two billion parameters, making it the backbone for other downstream applications the company builds today. This ability to use foundation models as the AI and data encyclopedia for smaller applications will ultimately propel the field of digital pathology forward by widening the playing field.
Going multimodal
To address more complex predictive problems, additional data types can be integrated. Clinical, radiologic, or genomic data can be combined with morphologic embeddings or used during training to help the model learn which tissue features carry a signal of disease or identify a biomarker. These approaches aim to support precision oncology by making morphologic data computable and aligning slide-derived features with other cancer-focused datasets. “These approaches can surface subtle or ‘latent’ patterns in pathology slides and align them with other data sources,” Norgan said. Pathologist and oncology care teams can then evaluate and interpret the features identified by the models within the clinical and biological context.
“In this way, pathologists and oncology teams use these outputs as decision-support tools, while clinical judgment remains central to diagnostic interpretation and therapeutic decision making,” Norgan added.
Atlas has now been succeeded by Atlas2, which was trained on 5.5 million pathology images and is now a two billion-parameter model, making it one of the largest pathology foundation models to date. The team has explored distilling methods to create smaller, more efficient, and targeted versions of the model that retain performance, with an eye toward finding a balance between scale and deployability.
Proscia is embarking on a different multimodal approach that combines vision models with language models, with the intent of creating methods to query the morphology of digitized slides. Their efforts in vision-language models (VLMs) combine textual data with visual data and allow the model to describe the morphology of a slide, answer questions about what it contains, find images in a database based on a text query, and even follow multimodal instructions such as “circle the tumor area on this image.”
In short, a VLM can be engaged in the same way you can engage a human. “I could go ask a pathologist to point out all the areas of tumor-infiltrating lymphocytes,” West said. “Now, because language-vision models are encoding language and images in the same space, they can do that, too. You can ask the model to describe what is happening in an image, and it will tell you exactly what it sees.”
At Cedars-Sinai, Omar’s work with large language models takes a less direct route of leveraging queries to gather information from research studies or even images. “Basically, you could go to the tool, ask questions, and the tool will provide you with pieces of code,” he explained. “These pieces of code are what you use on the slide to get more information.”
Atlas provides a similar function at the Mayo Clinic, Norgan noted. Because the model-generated embeddings in the digitized slide also encode semantic information, the Atlas team is now building a slide search function, which would allow researchers or clinicians to identify and access slides, or regions of slides, with related features.
Democratizing care
Although it will take time to disseminate the tools needed for AI-enabled digitized models of cancer care to smaller health systems, the future is now at Moffitt Cancer Center, where the research hospital is engaged in a top-to-bottom digitization of its system.
Marilyn Bui, MD, PhD Senior Member Moffitt Cancer Center
According to Marilyn Bui, MD, PhD, senior member of the departments of pathology and machine learning, the comprehensive cancer center plans for full digital adoption across clinical and research labs by 2027. Last August, it entered a multi-year collaboration with integrated AI and digital pathology company PathAI to deploy its cloud-based digital pathology image management system for both research and clinical applications.
Within the pathology department, the transition will mean that all glass slides will be scanned and reviewed digitally, providing the basis for applying AI computational tools to assist pathologists. Bui said that the cancer center is accelerating its move toward clinical AI adoption: “Just today I received an email asking which AI algorithms we plan to incorporate for clinical utility—prostate cancer, breast cancer, general tumor detection,” she said. “For us, it’s no longer just research.”
Moffitt is taking a hybrid approach to algorithm development and deployment within the system. Some AI tools will come from commercial vendors and will be validated internally, while others will be developed by investigators through the center’s translational pathology work. Taking this approach will allow it to apply AI to both common cancers and the rare tumor types Moffitt frequently encounters.
While the digital initiative will be transformational, Bui emphasized that the goal is not to replace pathologists but to enhance their capabilities. She prefers to refer to AI as augmented intelligence to reflect this. “Artificial intelligence suggests a robot replacing us,” she said. “But what we mean is augmented intelligence—tools that assist and enhance our ability to make clinical decisions.”
Further, Moffitt intends to integrate digitized slide data with genomic, proteomic, and clinical outcome data to build a multimodal data environment that could advance precision oncology. “Digital pathology and AI will allow us to extract far more information from tissue samples,” Bui said, “making our diagnoses more actionable for the clinical team and ultimately improving patient care.”
The promise of AI in oncology isn’t just better algorithms, it’s broader access. The maturation of computational pathology and its dissemination from large cancer centers like Moffitt to regional and rural health systems has the potential to provide levels of care typically only available at large research hospitals in community settings as well.
“It’s about democratizing access to care,” said Omar. “For a person in Maine or Wisconsin or another place to have access to the same high-quality care that you would get from a larger academic medical center in LA or New York, slides have to be digitized.”
Over the next 10 years, there could be a compelling business case for hospitals to embrace digital pathology. As the cost of scanners comes down and a broad range of diagnostic tools becomes available, digitizing routine H&E slides could become common.
While genetic cancer testing can cost hundreds of dollars, Omar pointed out that pathology slides “cost $5 [and] they are available universally, in all patients with cancer.” As AI models increasingly identify genomic-level insights directly from those inexpensive images, it represents a “huge win for accessibility, making AI work for patients who cannot afford genetic tests,” Omar said. If there is broad adoption of digital pathology “it is very easy to roll out any kind of AI models and computational tools across the board, across situations and locations that don’t have access to care.”
“At the end of the day, all slides will be digitized,” he concluded. “It’s just a matter of time.”
Chris Anderson, a Maine native, has been a B2B editor for more than 25 years. He was the founding editor of Security Systems News and Drug Discovery News, and led the print launch and expanded coverage as editor in chief of Clinical OMICs, now named Inside Precision Medicine.
According to the World Health Organization, an estimated 1.4 billion adults aged 30–79 worldwide had hypertension in 2024, representing around one-third of the global population of that age. Of these, 44% were unaware that they were living with a leading risk factor for premature death and poor health worldwide due to its association with myocardial infarction, stroke, and kidney disease.
Despite the size of the hypertension problem, its diagnosis and treatment pathway has remained largely the same for decades.
A 60-year-old pathway
“The current pathway in hypertension diagnosis and treatment has really not changed in over 60 years,” said Sandosh Padmanabhan, MD, PhD, chair of pharmacogenomics and professor of cardiovascular genomics and therapeutics at the University of Glasgow in Scotland.
He explained that it is based on opportunistic detection of hypertension, which has traditionally been defined as a blood pressure (BP) of 140/90 mmHg in the clinic, although thresholds vary by measurement method and guideline. For example, out-of-office measures typically use lower cut-points (e.g., home/daytime ambulatory averages) of 135/85 mmHg.
Sandosh Padmanabhan, MD, PhD Professor University of Glasgow
Diagnosis typically occurs when a patient visits their primary care physician (PCP) or has a pharmacy BP check. Confirmation follows, ideally with out-of-office BP monitoring to avoid misclassification caused by one-off measurements.
Patients are then stratified by predicted 10-year cardiovascular risk, using risk calculators such as Q-risk or the PREVENT score, and treatment is based on a stepwise algorithm. First, patients are generally given lifestyle advice like reducing salt, alcohol, and caffeine intake, improving sleep, managing stress, and increasing exercise. This may give them a chance to reduce their BP without pharmacologic intervention.
If unsuccessful, depending on local guidelines, patients may be offered an angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker if under 55 years of age. Those over 55 years or of Black African or Caribbean origin are started on a calcium channel blocker. The next steps combine ACE inhibitors and calcium channel blockers, then add a thiazide-like diuretic, followed by spironolactone or other drugs.
However, this approach uses “a population-level logic,” said Padmanabhan. Although age and ethnicity are considered, “these are broad demographic proxies that don’t include any understanding of the individuals’ underlying pathophysiology or the genetic makeup.”
He stresses that, on a public health basis, the system works. There are multiple effective, low-cost antihypertensive drug classes and many generic options available that effectively lower BP. Despite this, control rates are poor. “Fewer than one in four hypertensive adults globally have their BP adequately controlled,” he said.
The measurement problem
Part of the issue lies in how BP is measured. “To give you an idea about the scale of inertia, we diagnose BP using a device that was introduced in the late 19th century,” Padmanabhan noted, referring to the sphygmomanometer invented by Scipione Riva-Rocci in 1896. Not only that, the technique can also be flawed. Variables such as incorrect cuff size, improper positioning, and patient movement can distort readings. Even talking during measurement can increase BP values by 5–9 mmHg or even higher.
Crucially, a single measurement provides little insight into cumulative lifetime exposure to high BP and can be skewed by issues like white coat hypertension or masked hypertension. “We look at the BP number, but the patients don’t experience that number. What they experience is a lifelong vascular risk,” Padmanabhan explained. “Treatment is not about a short-term reduction in a number. It’s about long-term sustained risk reduction.”
Yet the current system remains reactive and is not working well enough. “We have to move away from reactive diagnosis to proactive identification,” Padmanabhan said. “The earlier we measure accurately and respond systematically, the fewer surprises we’ll see later.”
Continuous monitoring
The pitfalls of opportunistic, or even planned, BP measurement are driving the emergence of new technologies capable of continuous monitoring.
Josep Solà, PhD CTO and Co-founder Aktiia
Josep Solà, PhD, began working on optical sensing technology in 2004 at the Centre for Electronics and Microtechnology in Switzerland. By analyzing subtle changes in reflected light caused by arterial dilation, it became clear that BP could be measured using these light signals. In 2018, this research was spun out into Aktiia, where Solà is CTO and co-founder. The company has developed and commercialized the Hilo band: a CE-certified wearable medical device designed for continuous, cuffless, BP monitoring that has been clinically validated against traditional ambulatory BP monitoring.
The band tracks BP and heart rate automatically, about 25 times per day, without requiring any action from users. Paired with an app, the device shows users daily, nightly, and long-term BP trends. It is currently available as a certified medical device across Europe, Australia, and Canada, and, following FDA approval in July 2025, the company is preparing for a U.S. launch.
Solà said he and co-founder Mattia Bertschi, PhD, were convinced they could change how hypertension is being managed today. He believes there is no good reason why most people with hypertension cannot control the condition. The medication is cheap and effective; the problem is that there has been no technology that patients can use to properly manage their condition.
“No one wants to use a cuff every day for the next 30 years,” said Solà. “They’re just so inconvenient, and you cannot expect people to proactively measure something they don’t feel.”
The Hilo band gives wearers a feedback loop that has historically been missing from BP measurement. Users can immediately see that reducing their salt or alcohol intake, for example, lowers their BP. “We are empowering people,” said Solà. “We are empowering them to look at the intervention, or combination of interventions, with or without medication, to see what is effective for them, and this reinforces their willingness to continue with the changes they are making.”
Credit: Hilo
Data published by Aktiia has shown that this approach works. A study of 8,950 U.K.-based Hilo users indicated that individuals who monitored their BP continuously showed better control over time. Specifically, users over 50 years of age appeared able to prevent the age-related rise in systolic BP typically seen in the general population, which the researchers say “may reflect greater awareness, stronger treatment adherence, and lifestyle changes prompted by continuous feedback.”
Wearables at scale: Opportunity and caution
Beyond dedicated monitoring devices like the Hilo band, smartwatches and other devices are increasingly capable of detecting physiological signals associated with cardiovascular risk. The Apple Watch can detect potential signs of chronic hypertension by analyzing heart rate sensor data over 30-day periods, the Huawei Watch D provides on-demand and 24-hour ambulatory BP monitoring using an air-filled strap, while the team behind the Oura ring is developing a “Blood Pressure Profile” feature to detect early signs of hypertension.
Although this represents a significant step toward embedding cardiovascular monitoring into everyday life, the increasing use of these devices raises important questions about accuracy, interpretation, and clinical integration, particularly as they often rely on indirect signals rather than direct BP measurement.
Adam Bress, PharmD Researcher University of Utah
As Adam Bress, PharmD, from the Spencer Fox Eccles School of Medicine at the University of Utah, and colleagues have recently shown, translating wearable-derived signals into meaningful clinical information is not straightforward.
They evaluated the hypertension alert feature of the Apple Watch, which has a published sensitivity of 41% and specificity of 92%, meaning that approximately 59% of individuals with undiagnosed hypertension would not receive an alert, while about eight percent of those without hypertension would receive a false alert.
“The problem there, is that this data only tells you how the alert works in a very controlled, limited population,” said Bress. “In order to understand how it’s going to work in the real world, we need to know how the true prevalence of undiagnosed hypertension varies in the population and in subgroups and to what degree.”
Using data from nearly 4,000 adults in the U.S., Bress and colleagues showed that the pretest probability of having hypertension has a significant impact on the reliability of the alert. For example, among adults under 30 years of age, the pretest probability of having hypertension is 14%. A positive alert on the Apple Watch would increase this probability to 47%, whereas no alert reduces the probability to 10%.
However, for adults aged 60 years and older, an alert increases the probability of an individual having hypertension from a pretest level of 45% to 81%, whereas the absence of an alert only lowers it to 34%. This translates to large numbers of false negatives when applied across millions of users.
In Apple’s validation study, the company stresses that the watch is not intended to replace traditional diagnosis methods or to be used as a method of BP surveillance, and that the absence of a notification does not indicate the absence of hypertension.
“The concern is, if you’re not getting an alert, will people interpret that as them not having hypertension,” said Bress. “That’s the worry. … The groups in which the negative alert is the least trustworthy contain the people with the highest risk. We’re most worried about people being falsely reassured.”
At the same time, he is clear that wearables should not be dismissed. “This technology is an important step forward; we need more wearable tech that can screen,” he said.
Unfortunately, access to these devices is not universal. Advanced monitoring technologies are often first adopted by the “worried well”—people who are more affluent and health-conscious—rather than those at highest risk.
“The only thing that can change this is a clear political decision to make awareness of hypertension large scale,” said Solà. Devices like the Hilo band could be used much like the continuous glucose monitors for diabetes. The difference is that if someone with diabetes doesn’t keep their blood glucose levels under control through regular monitoring, they can become ill very quickly. With hypertension, the effects of poor control don’t become apparent for decades.
“We need the policymakers to understand that investing in this technology today will have a return on investment in 10 years from now, not in one year from now,” Solà remarked.
Targeted drug selection
Even when hypertension is detected early and monitored closely, treatment remains largely empirical and can lead to therapeutic inertia, one of the biggest current challenges in hypertension care. “BP is not like diabetes, it doesn’t cause symptoms, and because of that, we don’t escalate treatment often enough,” said Padmanabhan.
At the same time, treatment selection remains largely trial-and-error. Clinicians cycle through medications sequentially, adjusting regimens based on response rather than underlying biology. The issue is that failed attempts risk side effects and can erode trust. That lack of trust can then impact adherence and, therefore, cardiovascular risk.
Instead, Padmanabhan believes that we need to move toward mechanistically informed drug selection.
This approach is common in oncology, where targeted therapies have been matched to specific mutations, but the picture is more complex for BP. Genome-wide association studies (GWAS) have identified more than 30 genes associated with monogenic forms of hypertension or hypotension and more than 2,100 single nucleotide polymorphisms linked to BP regulation, underscoring its highly polygenic nature.
This, combined with the strong influence of environmental factors, means that there is no single pathway or biomarker that can be easily targeted to reduce BP.
Padmanabhan’s work on the uromodulin gene (UMOD), however, shows that GWAS data can translate into therapy. His team identified a signal on chromosome 16 linked to uromodulin, a protein that is only expressed in one part of the kidney and plays a role in salt regulation. In a clinical trial comparing people with low BP to those with high BP, they found that people with the UMOD allele that increases protein expression experienced a sustained reduction in BP when treated with the loop diuretic torasemide, whereas the effect was only temporary and followed by rebound in those carrying the UMOD allele that lowers protein expression.
Approximately two-thirds of the population carry the UMOD allele that increases protein expression, meaning that loop diuretics like furosemide or torasemide, which are more commonly used to treat heart failure, could potentially be used in hypertension personalized by the patient’s genotype.
So far, “this is the only clinical trial from a GWAS-identified genetic variant in hypertension,” Padmanabhan noted, highlighting both the promise and challenge of pharmacogenomics in hypertension.
Although clinical translation from GWAS of hypertension has been limited, research has shown that genetic variation in drug-metabolizing enzymes can significantly impact hypertension treatment efficacy and toxicity. For example, variants of CYP2D6 affect metoprolol metabolism whereas those in CYP2C9 influence responses to losartan. Research is needed to determine whether testing for these variants or others could reduce trial-and-error prescription, minimize side effects, and thus increase patient confidence and long-term engagement.
Teresa Castielo, MD Director MIAL Healthcare
On a more fundamental level, biological sex differences remain a significant consideration in cardiovascular medicine. “Biological factors are an integral part of the clinical picture,” noted Teresa Castiello, MD, consultant cardiologist and director of MIAL Healthcare in London. She points out that clinical trials have historically seen a predominance of male participants; as a result, many standard medication dosages are based on data primarily derived from men.
This can lead to challenges with tolerability and a higher incidence of side effects in women as the therapeutic dose required for efficacy often tends to be lower in female patients.
Castiello suggests that this area of management warrants further refinement in clinical practice. She also emphasizes that key aspects of female cardiovascular risk, including reproductive history, menopause, and conditions like polycystic ovary syndrome, are nuances that may not always receive the necessary focus in routine care.
Toward a precise, preventative system
Ultimately, transforming hypertension care will require more than new technologies or therapies. It will require a fundamental change in how care is delivered.
Padmanabhan argues that hypertension should be managed through a “precision prevention service,” that integrates early detection, continuous monitoring, and personalized treatment, and involves more than just PCPs.
This approach recognizes that the disease is not just a clinical condition but a societal one, influenced by factors such as diet, socioeconomic status, work patterns, and access to care. Equity remains another critical issue. “We treat the ideal average patient under ideal circumstances but that’s not reality,” said Padmanabhan.
There also needs to be a cultural shift, said Castiello. “It’s not just the doctor’s responsibility; we also need to take responsibility for our own health.”
Solà shares a similar vision for the future: he would like to see BP measurement to become as routine as brushing your teeth, supported by technologies that empower individuals and reduce the burden on healthcare systems.
If realized, this shift could transform hypertension from a silent, progressive disease into a manageable, preventable condition, saving millions of lives in the process.
Laura Cowen is a freelance medical journalist who has been covering healthcare news for over 10 years. Her main specialties are oncology and diabetes, but she has written about subjects ranging from cardiology to ophthalmology and is particularly interested in infectious diseases and public health.
Dating back more than a century, biobanks have outgrown their beginnings as small, local collections to become large, global facilities that store and handle millions of samples and serve thousands of researchers at any given time. Over the years, biobanks have transformed from passive repositories into active research infrastructures that are increasingly bridging the gap between medical research and clinical applications.
“Today’s biobanks have evolved far beyond sample storage,” said Yan Zhang, PhD, president of proteomic sciences at Thermo Fisher Scientific. “They are automated, digitally connected systems integrated with hospitals and health networks to ensure appropriate consent, longitudinal clinical context, and the ability to re-engage participants over time.”
Yan Zhang, PhD President Thermo Fisher Scientific
As safeguards of clinical samples, biobanks fulfill a central role in the advancement of precision medicine. Access to the right samples can make or break a research project, with most researchers reporting that they have had to limit their scope of work because of difficulties obtaining the samples they need.
“Robust, population-scale biobanking enables precision medicine to move from isolated findings toward broader clinical relevance,” said Zhang. “Modern biobanks combine genomics, proteomics, and other high-dimensional omics platforms with robust data architecture, high-performance computing, and artificial intelligence (AI)-driven modeling. Dedicated data science teams integrate molecular data, longitudinal health records, and curated public datasets to generate biologically meaningful interpretations.”
Biobanks now provide the infrastructure needed to support population-scale, longitudinal studies that allow scientists to uncover molecular drivers of disease and understand their evolution over time to ultimately identify biomarkers, develop targeted treatments, and inform clinical decisions.
“We’re seeing researchers design studies with scale in mind,” Zhang noted. “They’re combining proteomics, genomics, and clinical data to generate insights that are both statistically powerful and relevant to real-world populations. There’s also a clear shift from searching for a single biomarker to building a more complete, systems-level understanding of disease.”
To navigate today’s rapidly shifting landscape and meet their core purpose of supporting cutting-edge clinical research, biobanks have to keep up with fast-moving targets. Going forward, moving from initial discovery to translation will remain the number one challenge in precision medicine. “Generating discovery insight is no longer the limiting factor,” said Zhang. “Validating, standardizing, and implementing those insights at scale is.”
A matter of scale
Martin K. Rutter, MD Deputy Chief Scientist UK Biobank
One of the most transformative shifts in biobanking over the past decade has been an exponential increase in the scale of data collection and sample storage. At the forefront of this expansion is the UK Biobank, which currently stores around 18 million samples from 500,000 participants, together with imaging and biomarker data, healthcare records, questionnaires, physical measurements, demographics, lifestyle, and environmental data collected over the course of 20 years. This depth of phenotyping is what makes the data so valuable to researchers worldwide, said Martin K. Rutter, MD, professor of cardiometabolic medicine at the University of Manchester and deputy chief scientist at the UK Biobank. “When you link all that together, you can get amazing insights into the biology of disease.”
To keep up with increasing storage needs and researcher requests, the UK Biobank is now getting ready to move more than 10 million samples currently stored in its main laboratory to a new building in central Manchester by the end of the year. The new storage facility is designed to quadruple sample retrieval speed while making the whole infrastructure more energy-efficient and environmentally friendly.
The scale at which facilities like the UK Biobank operate today would have been unthinkable when it was established two decades ago. Such massive growth has been driven by rapid technological advances across genomics, transcriptomics, and proteomics, with costs continuing to fall while coverage, speed, and accuracy keep surging.
Partnerships with the pharmaceutical industry have also been instrumental in nurturing this exponential growth. This can be seen in initiatives like the UK Biobank Pharma Proteomics Project (UKB-PPP), a collaboration between the UK Biobank and 14 biopharmaceutical companies with the goal of analyzing proteomics data from 600,000 samples.
In the long run, scale provides the backbone to enable increasingly ambitious, statistically powerful studies. However, as they grow, biobanks face the challenge of navigating a constantly shifting landscape while making sure the samples and data they collect, store, and maintain are valuable to the entire research community they serve.
“Our job is to make the data available to researchers,” said Rutter. “We are involved now more than ever in connecting with research teams and trying to understand what their needs are.”
Through surveys and consultations, the UK Biobank actively gathers information to design prospective data collection programs that anticipate researcher needs. Next year, the biobank is planning a repeat assessment of its whole cohort, focusing on measurements of aging. The goal is to support researchers looking into causal pathways and mechanisms driving age-related diseases, empowering the development of preventive interventions and new diagnostics and treatments for age-related conditions.
Keeping pace with the evolving demands of researchers, industry, and the broader public is essential for biobanks to secure the funding necessary not only to operate but also to expand such vast enterprises, which remains a major challenge across this resource-intensive field.
Diversity takes the spotlight
Historically, samples collected by biobanks are biased in favor of participants who are white, middle-class, and have a higher education. This creates major disparities in the applicability of clinical research. In fact, studies have shown that patients from non-European ancestry backgrounds have not benefited equally from precision drugs approved by the U.S. Food and Drug Administration (FDA) to treat a range of cancer indications.
Even within biobanks dedicated to sampling the population of a specific region, ethnic minorities, low-income, or elderly people are often underrepresented, skewing results against the real-world populations they strive to serve. As the research community increasingly recognizes the importance of more diverse and representative patient cohorts, demand is rising for resources that address these barriers.
Representation is at the heart of All of Us, a program launched by the National Institutes of Health in 2018 to address the gap present at the time in many biobanks and sample repositories. This precision medicine initiative was designed to enroll participants who reflect the full range of populations found within the U.S., including individuals of varied ancestry backgrounds as well as those living in rural commmunities, which are rarely represented in biorepositories due in part to longstanding barriers to research participation, such as the logistical challenges of collecting samples and data from participants in remote locations.
Joshua C. Denny, MD CEO All of Us
“A lack of diversity impoverishes discovery and applicability of findings for all,” said Joshua C. Denny, MD, CEO of the All of Us Research Program.
For instance, data collected by All of Us has been used to investigate APOL1 gene variants linked to kidney disease, which are more common among people of West African ancestry. This research led to the identification of a novel APOL1 variant that can reduce the risk of kidney disease in individuals carrying high-risk variants.
The program has so far enrolled about 870,000 participants across all U.S. states, with about 80% of them representing communities that have historically been underrepresented in biomedical research. This has been achieved by emphasizing accessibility and flexible participation models; participants can enroll digitally and choose whether to share access to their electronic health records, donate biospecimens, and complete demographics and lifestyle surveys. They may also opt to provide saliva samples, simplifying logistics in rural areas with limited access to blood collection facilities.
“What works in a rural location is different from what works in a big city like New York,” said Denny. Whether it comes to location, age, or language, he emphasized the importance of adapting how the program approaches and engages each population.
Democratizing access to patient data across the research ecosystem is another major biobanking challenge that All of Us is committed to addressing. The program has established a streamlined access model that enables researchers to access the data they need in less than two hours if they belong to one of the 1,300 already approved institutions across the world. Together with central data storage and cloud-based analysis tools, their setup is designed to make the data accessible to researchers lacking the resources and local infrastructure for high-performance computing.
Towards global integration
With precision medicine studies steadily escalating both in size and complexity, researchers increasingly seek to bring together data stored across diverse biobanks to power larger, more ambitious studies with broader scientific and societal impact. However, building the infrastructure needed to enable cross-biobank studies is still a challenge, starting with convening stakeholders to harmonize data collection standards and establish international guidelines.
Anticipating this need, in 2013 the European Union established the Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium (BBMRI-ERIC), which currently coordinates the activity of about 500 biobanks across 32 countries.
Jens K. Habermann, MD, PhD Director General BBMRI-ERIC
“Precision medicine can only move forward with a strong starting point for research,” said Jens K. Habermann, MD, PhD, professor for translational surgical oncology and biobanking at the University of Lübeck and director general of the BBMRI-ERIC. “It can be very difficult for scientists to get all the information they need in one place, and this is what biobanks can enable.”
Pulling together data from all its members, the BBMRI-ERIC has set up a central catalogue for biobanks, biomolecular resources, and other data and sample collections, which users can employ to identify relevant resources and build virtual cohorts tailored to their research needs. The consortium also works with international committees to set guidelines and support members working towards compliance with international standards.
Despite ongoing progress, there are still obstacles ahead when it comes to harmonizing biobanking practices worldwide, including data collection, annotation, storage, and sharing. Tackling differences in data protection, consent, ethical standards, and regulatory requirements across borders will be another necessary step towards broader standardization. Finally, biobanks will need to invest in cybersecurity to ensure patient data can be shared between institutions safely.
Funding will be key to successfully addressing all these challenges. On this front, biobanks face the difficult task of maintaining their existing infrastructure, staying up to date and relevant to the research community, and investing in cross-biobank initiatives. All this must be balanced with growing financial pressure on research centers, hospitals, and the governments supporting them.
As part of its 10-year roadmap, the BBMRI-ERIC is setting the goal of forming international networks that bring together more diverse biobank types, such as environmental, wildlife, veterinary, and plant biodiversity repositories. The overarching aim is to move towards a One Health approach to biobanking, where samples and data that expand beyond monitoring human populations are brought together to tackle overlapping challenges that simultaneously affect human, animal, and environmental health.
Data-driven horizons
As the field forges ahead, biobanks are undergoing broad transformations in the way they operate. On the technology side, these changes are being propelled by the rise of multi-omics techniques in precision medicine research, as well as by rising demand from the research community for non-invasive patient monitoring data and longitudinal sample collection. All of these will be critical for the development of the next generation of personalized therapies and diagnostics.
“Over the next decade, biobanks are expected to become increasingly integrated into clinical and translational workflows,” said Zhang. “Proteomics, in particular, will play a growing role in helping us understand the dynamic biology of disease, enabling earlier detection, better prediction of recurrence, and more precise therapeutic strategies.”
A key driver of this shift will be AI. No longer just a supporting tool, AI is now becoming an integral part of biobank operations, contributing to real-time sample monitoring, predictive maintenance, risk management, and decision making.
On the data analysis side, Zhang has seen how AI is redirecting the focus from data generation to data interpretation. She said, “Biobanking has already enabled the collection of high-quality biospecimens linked to large-scale molecular and clinical datasets. The challenge now is extracting meaningful biological insight from that complexity.”
Although still in its early days, AI is becoming central to how researchers make use of biobank data, noted Rutter. Drawing from the UK Biobank data, recent studies have developed AI models that can predict a patient’s risk of stroke based on retinal images, calculate the risk of future disease by looking at an individual’s disease history, or spot neurodegenerative diseases like Alzheimer’s and Parkinson’s early using brain scans and physical activity data.
Going forward, Rutter expects to see biobanks moving away from static cohorts and in favor of continuous data collection, enabling more powerful predictions. For example, the UK Biobank is developing a mobile app that can track a participant’s physical activity and monitor their location and sleep patterns, offering an in-depth look at how a variety of factors affect their health with much more accuracy than self-reported surveys.
Over time, all these advances will steer clinical practice from treatment to prevention, allowing healthcare professionals to act early in the patient journey, when interventions are most effective, and eventually, even before disease develops. Ultimately, addressing complex diseases will require coordinated contributions from all stakeholders, including AI innovators, drug developers, clinicians, technology providers, and policymakers.
“The next decade will be incredibly exciting,” said Denny. “It will be all about leveraging the huge scale of resources that are just emerging today.”
Clara Rodríguez Fernández is a science journalist specializing in biotechnology, medicine, deeptech, and startup innovation. She previously worked as a reporter at Sifted and editor at Labiotech, and she holds an MRes degree in bioengineering from Imperial College London.
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