Animal testing has shaped modern drug development for decades, but its limitations become especially visible when scientists evaluate cancer immunotherapies. These treatments are designed to stimulate the immune system to recognize and destroy malignant cells, yet the same powerful immune activation can trigger life-threatening complications in patients. Researchers at the University of Rochester are developing a human-cell-based alternative: miniature tissue models known as organs-on-a-chip that could help predict these toxicities before experimental medicines reach clinical trials.

The work is being led through the university’s Translational Center for Barrier Microphysiological Systems, or TraCe-bMPS. The center is building drug-development tools around modular µSiM tissue chips, devices that contain ultrathin membranes populated with human cells. Unlike conventional laboratory cultures, these systems are engineered to reproduce key features of biological barriers, including the physical separation between tissues, controlled exposure to drugs, and measurable inflammatory responses. Their modular construction also allows the devices to be manufactured in large quantities with consistent dimensions and materials.

The platform is based primarily on acrylic components and incorporates membranes designed to mimic interfaces found in the human body. Such barriers regulate the movement of molecules and immune cells between compartments, making them central to drug absorption, toxicity, and inflammation. By assembling reproducible chips with human cells, researchers can study how a therapy affects a tissue under conditions that more closely approximate human biology than many animal models. The design also makes it possible to modify individual components for different organs or disease settings.

A major feature of the Rochester system is the integration of sensors directly into the chips. Developed in part through the work of biomedical engineering professor Benjamin Miller, these sensors can track barrier function and inflammatory signaling in real time. Instead of waiting until the end of an experiment to examine damaged cells, researchers can observe how a tissue responds as a drug is introduced. Changes in electrical or molecular signals may reveal that a barrier is becoming more permeable or that inflammatory pathways are being activated, providing an early warning of toxicity.

The immediate focus is on two severe complications associated with some cancer immunotherapies. Cytokine release syndrome, commonly called CRS, occurs when activated immune cells release large quantities of signaling proteins known as cytokines. The resulting systemic inflammation can cause fever, low blood pressure, breathing difficulties, and, in the most serious cases, organ failure. Immune effector cell-associated neurotoxicity syndrome, or ICANS, can affect the nervous system, producing confusion, seizures, impaired speech, or other neurological symptoms. Both conditions are difficult to predict reliably with animal testing because species differ in immune signaling networks and cell-surface receptors.

“The goal is to predict these toxicities from human cells on a chip, before a drug ever reaches a patient, and to do it without relying on animal models that have repeatedly failed to predict CRS in people,” says James McGrath, director of TraCe-bMPS and the William R. Kenan Jr. Professor of Biomedical Engineering. The approach does not simply attempt to reproduce a whole human body in miniature. Instead, it focuses on specific biological barriers and immune interactions that are directly relevant to the safety question being studied.

The project has now reached an important regulatory stage. The team was accepted into the US Food and Drug Administration’s Innovative Science and Technology Approaches for New Drugs, or ISTAND, pilot program. ISTAND is intended to help evaluate emerging methods that could improve drug development and eventually support regulatory decisions. Acceptance does not mean that the tissue-chip platform has already been approved for routine use, but it creates a formal pathway for the researchers to work with the FDA on defining the evidence needed for qualification.

The Rochester group is preparing a detailed qualification plan that will describe clinical considerations, timelines, data-sharing procedures, and statistical methods for evaluating the technology. If the platform ultimately receives FDA qualification, pharmaceutical companies could use its results as part of the evidence submitted in support of new drug applications. Such recognition would represent a significant step for organs-on-a-chip research, which has expanded rapidly but has only rarely progressed into formal regulatory programs.

The effort comes as US regulators seek alternatives to animal testing in preclinical safety studies. The FDA Modernization Act 2.0, enacted in 2022, opened the door for non-animal methods, while the agency’s 2025 roadmap outlined further efforts to reduce reliance on animal models. Researchers say pharmaceutical companies are showing strong interest because many immunotherapies are already in clinical development and carry risks of CRS or ICANS. The Rochester team collaborated with Graham Marsh of the Critical Path Institute and consulted with scientists at Pfizer about how the tool could fit into real-world drug development. If successful, the technology could give researchers a more human-relevant way to identify dangerous immune reactions early, helping developers eliminate unsafe candidates and refine promising treatments before patients are exposed.

Subject of Research: Human-cell tissue chips for predicting cancer immunotherapy toxicities.

Web References: University of Rochester; Translational Center for Barrier Microphysiological Systems; University of Rochester Biomedical Engineering; FDA ISTAND pilot program; FDA Modernization Act 2.0.

Image Credits: URochester photo / J. Adam Fenster.

Keywords: Drug discovery, tissue chips, organs-on-a-chip, cancer immunotherapy, cytokine release syndrome, CRS, ICANS, neurotoxicity, animal testing alternatives, biomedical engineering, microphysiological systems, human-cell models.

The University of Houston has launched UH Health, a cross-disciplinary academic health enterprise intended to reshape how healthcare professionals are educated, how medical research is conducted and how care reaches communities across Texas. The initiative brings together medicine, nursing, pharmacy, optometry, psychology, social work, population health, biomedical research and health systems science under a single interprofessional framework. University leaders say the model is designed to confront a rapidly intensifying combination of population growth, healthcare worker shortages, chronic disease and unequal access to medical services.

Rather than treating healthcare disciplines as separate academic and clinical systems, UH Health is built around team-based care. In this approach, physicians, nurses, pharmacists, optometrists, behavioral health specialists, social workers and public health experts contribute to a shared understanding of each patient’s needs. The model reflects the growing recognition that health outcomes are shaped not only by disease biology, but also by mental health, housing, income, nutrition, transportation and access to preventive services. By training students to work across professional boundaries, the University hopes to prepare graduates for a healthcare system increasingly focused on coordinated and personalized care.

“Healthcare is no longer delivered in isolation, and complex health challenges require coordinated solutions,” said Jonathan McCullers, vice president for health affairs at UH and dean of the Tilman J. Fertitta Family College of Medicine. The university’s academic health strategy is intended to place students in an environment where clinical reasoning, behavioral science, population health and scientific discovery interact. Such integration can help future clinicians identify the underlying drivers of illness, select more appropriate interventions and communicate more effectively with specialists and community organizations.

The launch comes as Texas faces a widening gap between healthcare demand and workforce capacity. Rapid population growth, an aging population and shortages of trained professionals have placed pressure on hospitals and clinics throughout the state. The Texas Hospital Association has reported that 64% of hospitals were operating with fewer beds or reduced services because of staffing shortages. UH Health aims to expand the pipeline of physicians, nurses, pharmacists, optometrists, social workers and other professionals while emphasizing workforce readiness, collaborative practice and the ability to manage complex cases.

UH’s location near the Texas Medical Center gives the initiative access to one of the world’s largest concentrations of hospitals, research institutes and specialized clinical programs. Partnerships with organizations including HCA Healthcare, Memorial Hermann Hospital, Baylor College of Medicine and MD Anderson Cancer Center are expected to provide students and researchers with expanded opportunities for clinical training, translational research and collaboration. Translational research—the process of moving discoveries from laboratories into diagnostics, treatments and public health programs—depends heavily on this type of interaction between universities, hospitals and industry.

The university is also positioning UH Health as a platform for research that reaches beyond traditional medical departments. Scientists in chemistry, physics and biomedical engineering are working alongside health researchers on problems ranging from cancer and infectious disease to substance-use disorders and neurodegeneration. UH researchers have received more than $11 million from the Cancer Prevention and Research Institute of Texas for work involving cancer prevention, diagnosis and treatment. A fentanyl vaccine developed at the university is advancing toward human trials, although its clinical effectiveness and safety will need to be established through carefully controlled studies.

Brain health is another major focus. Texas ranks fourth nationally in the number of Alzheimer’s cases and second in Alzheimer’s-related deaths, according to the Texas Department of State Health Services. More than 100 faculty members across 12 of UH’s 16 colleges are involved in research related to prevention, early detection, caregiving and treatment, supported by $62.5 million in active grants. Their work may involve diverse technologies and methods, including biological markers, computational analysis, behavioral interventions, biomedical engineering and the study of social factors that influence cognitive decline and caregiver health.

To strengthen this research ecosystem, UH is investing $77 million in a new 55,000-square-foot medical research building. The facility is expected to support interdisciplinary laboratories and shared scientific infrastructure, allowing investigators from different fields to exchange data, equipment and expertise more efficiently. In modern biomedical science, discoveries often emerge at the boundaries between disciplines—for example, when engineering approaches are applied to drug delivery, physical sciences are used to study disease mechanisms or data science improves early diagnosis.

UH Health’s community programs are intended to connect these academic and research activities with practical healthcare needs. The UH Health Family Care Center provides primary care and mental health services to the university and surrounding communities, including Third Ward, East End and South Houston. The Household-Centered Care program gives students experience through home visits with community caregivers, while the 3rd Ward Place of Wellness offers screenings, preventive services and health education. The College of Optometry extends access to vision care through community clinics and outreach programs serving children and families.

The enterprise is also expanding beyond Houston through a partnership with DHR Health in the Rio Grande Valley, a rapidly growing region that continues to face significant medical access challenges. University officials say the collaboration could support healthcare education, workforce development and research tailored to local needs. By linking professional training, scientific discovery and community partnerships, UH Health is presenting an academic health model designed for a state where medical demands are growing faster than conventional systems can respond.

Subject of Research: Academic healthcare education, biomedical research, healthcare workforce development and community health access

Article Title: University of Houston Launches UH Health to Integrate Education, Research and Community Care

Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/ed42117d-36c9-45cf-b2e3-443e4e08b680/Rendition/low-res/Content/Public

References: Texas Hospital Association; Texas Department of State Health Services; University of Houston

Image Credits: University of Houston and UH Health

Keywords: UH Health, University of Houston, healthcare workforce shortage, academic health, team-based care, biomedical research, cancer research, fentanyl vaccine, Alzheimer’s disease, community healthcare, nursing, optometry, pharmacy, medical education, Texas Medical Center

A new study has brought scientists closer to observing how inflammation during pregnancy may alter the earliest stages of human brain development. Using three-dimensional tissue models made from human fetal brain samples, researchers modeled maternal immune activation and identified the signaling molecule interleukin-17A, or IL-17A, as a major driver of abnormal cortical development. The findings, published in Nature Neuroscience, offer a human-based experimental system for investigating how immune signals outside the brain can influence the formation of neural circuits before birth.

Maternal immune activation refers to the biological response triggered when a pregnant individual experiences infection or significant inflammation. Epidemiological studies have associated certain prenatal inflammatory exposures with an increased likelihood of neurodevelopmental conditions, including autism spectrum disorder, schizophrenia, and intellectual disability. Yet the mechanisms behind these associations have remained difficult to study. Animal models have provided important clues, but differences between species can limit their ability to reproduce the timing, cellular organization, and molecular environment of the developing human cortex.

The researchers addressed this challenge with “brain cerebroids,” three-dimensional, ex vivo models that preserve features of developing human fetal brain tissue. Unlike simplified cultures composed of a single cell type, these structures retain a more complex arrangement of neural progenitors, immature neurons, and supporting cells. Their three-dimensional architecture allows cells to communicate and organize in ways that more closely resemble the fetal cortex, including the production and movement of neurons that ultimately establish the layered structure of the brain’s outer region.

To recreate aspects of maternal immune activation, the team exposed the fetal brain cerebroids to inflammatory conditions associated with immune signaling during pregnancy. The central focus was IL-17A, a cytokine produced primarily by activated immune cells, particularly a subset of T cells known as T helper 17 cells. Cytokines are small signaling proteins that coordinate immune responses, but they can also affect tissues beyond the immune system. In the developing brain, inappropriate or excessive exposure to such signals may influence cell proliferation, migration, differentiation, and the formation of connections between neurons.

The study found that IL-17A exposure was linked to disruptions in cortical development within the human cerebroids. These effects are described as “IL-17A-driven,” indicating that the cytokine was not merely associated with the developmental changes but played a functionally important role in producing them. The disturbances affected the organization and behavior of developing neural cells, processes that are essential for building the cortex. During normal development, neural progenitor cells generate new neurons in tightly regulated sequences, while young neurons migrate to their appropriate positions. Interference with either process can alter the structure and connectivity of later-forming brain tissue.

The results are especially significant because they were observed in human fetal brain material rather than inferred only from animal studies. Previous research in mice has implicated maternal immune activation and IL-17A in changes to cortical organization and behavior in offspring. However, the human cerebroid model provides an opportunity to examine whether similar immune mechanisms operate in human tissue, where the timing of neurogenesis, cell-type composition, and cortical architecture differ from those of rodents. The approach may also help reveal human-specific responses that conventional laboratory animals cannot capture.

The findings do not mean that IL-17A exposure during pregnancy inevitably causes a neurodevelopmental condition, nor do they establish that prenatal inflammation is a single explanation for such conditions. Brain development is shaped by a combination of genetic factors, immune signals, environmental influences, and developmental timing. The experiments also took place in an ex vivo model, outside the full physiological environment of a living pregnancy. Cerebroids cannot reproduce the placenta, maternal circulation, hormonal changes, or the complete immune system, all of which influence how inflammatory signals reach the fetus and how long they remain active.

Nevertheless, the model could become a powerful platform for testing mechanisms and potential interventions. Researchers may use it to determine how IL-17A changes gene activity in neural progenitors, whether it alters communication between neurons and glial cells, and which molecular pathways make some developing cells more vulnerable than others. Because the tissue is human and three-dimensional, it may also support studies of patient-specific genetic risk, allowing investigators to compare how different backgrounds respond to the same inflammatory signal.

The work also highlights a broader shift in neuroscience toward experimental systems that combine human tissue with controlled laboratory manipulation. Understanding prenatal inflammation is difficult in clinical studies because immune exposure cannot be ethically assigned or precisely timed in pregnant people. Brain cerebroids offer a way to isolate individual components of that complex biology while preserving more of the developing tissue’s natural organization than conventional two-dimensional cell cultures. As these models improve, they may help distinguish temporary developmental changes from persistent alterations in cortical circuitry and identify when immune signaling is most consequential.

By showing that IL-17A can disrupt human cortical development in a three-dimensional ex vivo system, the study gives researchers a clearer biological target in the search for links between prenatal inflammation and later neurodevelopment. Its most immediate contribution is not a new diagnosis or treatment, but a more precise experimental framework: one that connects maternal immune signaling to cellular events in the developing human cortex. That connection could guide future work on prevention, therapeutic timing, and the complex origins of developmental differences long before the first symptoms become visible.

Subject of Research: Maternal immune activation and IL-17A-driven disruption of human fetal cortical development in 3D ex vivo brain cerebroids.

Article Title: Modeling maternal immune activation in 3D ex vivo human fetal brain cerebroids reveals IL-17A-driven disruption of cortical development.

Article References: Assir, M.Z.K., Yanakiev, M., Gim, D.H. et al. Modeling maternal immune activation in 3D ex vivo human fetal brain cerebroids reveals IL-17A-driven disruption of cortical development. Nature Neuroscience (2026). https://doi.org/10.1038/s41593-026-02400-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41593-026-02400-2

Keywords: maternal immune activation, IL-17A, fetal brain development, cortical development, brain cerebroids, neurodevelopment, prenatal inflammation, human brain models, cytokines, neuroscience

Foundation models are promising to become the “universal translators” of biomedical data—but a new perspective argues that medicine should resist treating them as all-knowing digital doctors. In a review published in Nature Biomedical Engineering, researchers describe how large, adaptable artificial intelligence systems are reshaping biomedical imaging while warning that impressive benchmark scores can conceal serious weaknesses in clinical environments. Their central message is direct: foundation models are most likely to improve healthcare by augmenting specialists, not replacing them.

Foundation models are designed to learn broad representations from enormous and diverse datasets before being adapted to specific tasks. In biomedical imaging, those data may include magnetic resonance imaging, computed tomography, X-rays, ultrasound, digital pathology slides and ophthalmic images. The same underlying model could theoretically identify tumors, segment organs, estimate disease risk, retrieve similar cases and generate clinical reports. The vision becomes even broader when imaging is combined with pathology, electronic health records and genomic information, creating a composite system intended to analyze a patient across multiple biological scales.

That ambition reflects a major change from traditional medical AI. Earlier systems were generally built for one narrowly defined task, such as detecting pneumonia on chest radiographs or outlining a brain lesion on MRI scans. Foundation models instead attempt to create a reusable backbone that can be transferred across diseases, hospitals and imaging technologies. Technically, they learn statistical patterns in high-dimensional data, often through self-supervised training in which the system predicts missing, transformed or associated information. Afterward, developers fine-tune or prompt the model for particular clinical applications.

But biomedical imaging is not a single, uniform world. Images differ according to scanner manufacturer, acquisition protocol, patient population, disease prevalence and local clinical practice. A model trained primarily on data from large academic hospitals may perform very differently in community clinics, rural settings or countries with limited equipment. This problem, known as domain shift, can occur even when images appear visually similar. Small changes in image quality, contrast, hardware or patient demographics may alter the statistical distribution learned by the model and undermine its predictions.

The authors introduce a framework called real-world evaluation and assessment of foundation models, or REAL-FM, to examine whether these systems are ready for practical use. Rather than focusing only on accuracy scores, REAL-FM considers several dimensions at once, including the quality and representativeness of training data, technical readiness, clinical value, integration into existing workflows and responsible artificial intelligence. The framework is intended to help clinicians interpret claims about new models and to push developers toward evaluations that resemble actual medical practice.

One of the sharpest distinctions in the perspective is between pattern recognition and causal reasoning. Foundation models can become extraordinarily skilled at recognizing visual associations—for example, linking a particular texture or anatomical feature with a diagnosis present in their training data. Yet association is not the same as understanding why a disease occurs, how it will progress or whether a treatment will benefit an individual patient. A model may identify a correlation that is valid in one hospital but reflects a hidden confounder, such as a scanner type, reporting convention or patient-selection pattern, rather than a biological signal.

This limitation becomes especially dangerous when models are moved beyond simplified benchmarks. Many benchmark datasets provide carefully curated images, clear labels and narrowly defined tasks. Clinical care is messier: scans can be incomplete, diagnoses can be uncertain, records may contain contradictory information and several conditions may coexist. The perspective highlights a lack of verified generalization across such conditions, along with a shortage of prospective, outcome-based validation. In a prospective study, an AI system would be evaluated while care is actually being delivered, with researchers measuring whether it improves diagnostic accuracy, treatment decisions, patient outcomes or workflow efficiency.

Data scarcity is another obstacle. The largest foundation models require vast quantities of data, but medical information is difficult to collect, standardize and share. Patient records are fragmented across institutions, imaging data may lack reliable annotations and rare diseases are inherently underrepresented. Privacy regulations and governance requirements further complicate the creation of centralized datasets. As a result, a model may appear broadly capable while remaining poorly tested in the populations and clinical circumstances where errors could have the greatest consequences.

The researchers argue that human oversight therefore remains indispensable. In practical terms, foundation models may be most useful as clinical assistants that prioritize images for review, highlight suspicious regions, summarize longitudinal records or offer a second opinion that specialists can interrogate. Safe deployment will require transparent reporting of uncertainty, monitoring for performance drift, mechanisms for correcting errors and clear accountability when recommendations influence care. The future envisioned by the authors is not a single monolithic medical oracle, but a coordinated ecosystem of specialized AI systems, each evaluated for a defined clinical role and connected to expert-led workflows.

The perspective arrives as the biomedical AI field races toward increasingly general-purpose systems. Its warning is not that foundation models lack value, but that technical scale alone cannot establish clinical reliability. Before these models can be trusted with consequential decisions, they must demonstrate robustness across domains, usefulness in real workflows, safety under unexpected conditions and measurable benefits for patients. The proposed REAL-FM framework offers a way to separate viral demonstrations from durable medical progress—by asking not only what a model can recognize, but where it works, why it works and whether it makes care better.

Subject of Research: Foundation models and their real-world evaluation in biomedical imaging and clinical medicine.

Article Title: Foundation models in biomedical imaging: turning hype into reality

Article References: Muneer, A., Zhang, K., Hamdi, I. et al. Foundation models in biomedical imaging: turning hype into reality. Nature Biomedical Engineering 10, 1557–1575 (2026). https://doi.org/10.1038/s41551-026-01762-z

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01762-z

Keywords: foundation models, biomedical imaging, medical artificial intelligence, clinical AI, multimodal AI, domain shift, causal reasoning, clinical validation, responsible AI, REAL-FM

Meta has announced its intention to focus on open-weight large language models. Additionally, the company announced the release of an open model called Muse Glimmer and a promise to open the weights for Muse Spark 1.2, its more powerful model, in the next few weeks.

Alongside these announcements, Meta CEO Mark Zuckerberg published a more than 6,000-word essay outlining the company's philosophy about AI systems and governance moving forward. The essay aims to differentiate Meta from companies like OpenAI and Anthropic, which develop proprietary models and have lobbied the US government for help competing against large-scale distillation—which involves using an existing model to train a new one—or open-weight models by Chinese labs.

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