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Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer

OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet.

GPT-Red automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. The weak spots can then be patched before the final version of the software is released.

As LLMs become more complex and get used in a wider variety of tasks—especially in the form of agents, which can interact with computer files, websites, and third-party code as well as other agents—it’s hard for teams of people by themselves to keep up with all the types of attacks that might take place. “The risk surface grows and the blast radius also grows,” says Nikhil Kandpal, a research scientist at OpenAI who co-created GPT-Red.

OpenAI built GPT-Red to future-proof its safety testing process. “As more capable models become available, we will have already designed the system that can discover new modes of attack,” says Dylan Hunn, a research scientist at the company and fellow co-creator of GPT-Red. The researchers say it has already come up with new types of attack that had not been seen before.

OpenAI focused most of its efforts on a type of attack known as a prompt injection, where a hacker slips an LLM instructions to make it do things its developers or users do not want it to, such as copy confidential information, sabotage a company’s code base, or generate embarrassing or harmful output. In theory, such instructions can be hidden in any text that the LLM might encounter—in code or on a website, for example.    

Training dojo

To build GPT-Red, OpenAI’s researchers took an LLM that had not been trained as a hacker and set it up in what’s known as a self-play loop with several other models. Its goal was to try to attack the other models; their goal was to try to defend themselves. Over many rounds of play, GPT-Red became better and better at attacking other LLMs, and those LLMs became better and better at fending off the attacks.

The training took place in a kind of dojo that OpenAI had designed to mimic a range of scenarios in which LLMs might be deployed in the real world, including browsing the web, reading emails or calendar apps, and editing code.  

When GPT-Red found a new kind of attack, it would explore multiple different versions of it to find the most efficient one for specific scenarios. “Compared to a human red-teamer, the model is very, very good at finding exactly what will work, exactly what’s most effective,” says Hunn. “It’s extremely persistent about drilling down into an attack that it has discovered.”  

In particular, OpenAI claims that GPT-Red found a type of prompt injection attack that the researchers had not seen before, which they call a fake chain of thought. A chain of thought is a kind of diary in which an LLM makes notes to itself and keeps track of partial results as it works through problems. GPT-Red found a way to insert a fake entry into another model’s chain of thought that would trick that model into acting on spoofed information.

“It’s like if I told you that 1+1=3 and that you have verified this already,” says Chris Choquette-Choo, another research scientist on the team. “The model’s like, ‘Oh, okay, of course,’ and it just spits out 3.”

Jessica Ji, a senior research analyst who works on AI security at Georgetown University’s Center for Security and Emerging Technology (CSET), thinks the self-play loop that OpenAI used is a good approach. “The results look very promising,” she says.

OpenAI tested how good an attacker GPT-Red was by rerunning an experiment from 2025 in which human red-teamers tried to find weaknesses in an earlier version of GPT-5. When GPT-Red was set the same task, it was more successful at finding effective attacks than the humans had been.

OpenAI also tested GPT-Red against Vendy, a vending machine agent developed by Andon Labs, a company that assesses how well agents perform real-world tasks. GPT-Red was able to hack Vendy to make it change the prices of items on sale and cancel a customer’s order.

Defensive behavior

OpenAI says that when it tried out some of the strongest attacks that GPT-Red had come up with on its models, more than 90% of them worked against GPT-5 (released in August last year), and fewer than 23% worked against the new GPT-5.6.

GPT-Red isn’t perfect. It is not great at figuring out attacks that involve a back-and-forth conversation between hacker and target, something that human attackers would have few problems with. It is also not yet that great at using images, which can be used to pass text to models in prompt injection attacks.    

The company says that GPT-Red supplements the work of its human red-teamers. People can still find attacks it misses. One approach OpenAI is taking is to give GPT-Red an attack that humans came up with and ask it to find all the variations.

“I think human expertise will still be very important,” says CSET’s Ji. “It would be really useful to be able to distinguish where human testing is most needed.”

Unsurprisingly, OpenAI will not be releasing GPT-Red. The company is also confident that the super-hacker is stronger than any copycat model someone might try to create. The researchers say they have been working on the model for more than a year, backed by the compute resources of one of the richest companies in the world.

“It’s not a trivial thing that someone could easily do—you know, just go and train a super-attacker using this idea,” says Choquette-Choo.

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AI Model Predicts 348 Diseases from Electronic Health Record, Genetics

Research led by Harvard University shows an artificial intelligence algorithm can predict how likely it is that a given patient will develop 348 diseases based on data collected from electronic health records (EHRs) and genetic data.

As reported in Nature, the researchers built a new computational tool called ALADYNOULLI, a statistical machine learning model known as a Bayesian generative model, that jointly analyzes EHR data and genetics to model how disease risk evolves over an individual’s lifetime.

They applied it to over 683,000 participants across three independent biobanks, the UK Biobank, Mass General Brigham, and All of Us, covering 348 diseases and up to 52 years of follow-up.

ALADYNOULLI identified 21 disease signatures, which the researchers defined as clusters of conditions that tend to co-occur and evolve together over time, such as cardiovascular or metabolic disease. These were remarkably consistent across all three biobanks.

Crucially, the model also revealed biological subtypes within the same diagnosis: for example, early-onset and late-onset heart attacks showed distinct signature trajectories, suggesting different underlying mechanisms. Genetics also contributed to disease predictions, for example, in the case of cardiovascular disease the team found 23 cardiovascular variants missed by conventional single-disease analyses.

“We have painstakingly curated these signatures, which is a big differentiator. In contrast to deep learning approaches, which are typically ‘black boxes,’ our curated signatures capture the underlying biology in an interpretable way,” said co-lead author Giovanni Parmigiani, PhD, a Dana-Farber researcher and associate director of the Division of Population Sciences, in a press statement. “These signatures are then the drivers of the model’s ability to make predictions.”

For disease prediction, ALADYNOULLI substantially outperformed established clinical risk scores such as the Pooled Cohort Equation and PREVENT for cardiovascular disease, and the Gail model for breast cancer.

“There is a lot of useful information in a medical record, both over time and across different disease areas,” says Parmigiani. “That data would be difficult for a human to process in their head, but tractable for a machine learning model.”

Current medicine largely treats diseases in isolation and uses static, single-disease risk scores. ALADYNOULLI offers a unified, continuously updating health prediction for each patient that integrates their full diagnostic history and genetic predisposition simultaneously. This has major implications for earlier and more precise risk prediction, patient stratification in clinical trials, and genetic discovery.

“People are thinking about what their health is going to look like over the next few years, especially with increasing intervention options. This tool offers a path toward improving the prediction of future diseases so doctors and patients can take action to try to prevent them,” said co-senior author Alexander Gusev, PhD, a Dana-Farber scientist.

The team behind the model is now working to make the model broader and more accurate. They are also looking for opportunities to test the model in clinical practice and to help design better clinical trials.

The post AI Model Predicts 348 Diseases from Electronic Health Record, Genetics appeared first on Inside Precision Medicine.

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Modular Modeling Drives Smarter mRNA Manufacturing

Researchers are increasingly turning to modular mechanistic models to unlock greater efficiency and robustness in mRNA manufacturing, offering a more flexible way to optimize in vitro transcription (IVT) while reducing costly experimental work. According to Wei Xie, PhD, associate professor of mechanical and industrial engineering at Northeastern University, and her colleagues, modular approaches can increase productivity and product quality.

“A modular modeling approach simplifies the complex IVT reaction network by dividing it into discrete, reusable, mechanistically defined steps,” Xie said. “This structure improves mechanistic understanding by clarifying how each step impacts key quality attributes, including yield, capping efficiency, and transcript integrity.”

Rather than relying on a single monolithic model, the framework separates IVT into individual components, such as initiation, elongation, and termination, as well as parallel processes including mRNA degradation and precipitation. Each module can be independently calibrated, validated, and refined as new experimental data become available, allowing researchers to continuously improve predictive performance without rebuilding the entire model.

The modular architecture also lends itself to the evolving nature of mRNA therapeutics. Because the framework mirrors the modular structure of nucleic-acid sequences, it can be rapidly adapted for new constructs, accelerating process development for emerging vaccines and therapeutic candidates while minimizing redevelopment effort. Beyond improving process understanding, the model provides a powerful diagnostic platform for identifying production bottlenecks that constrain yield, productivity, or product quality.

The framework combines Shapley value-based sensitivity analysis, residual analysis, and simulated reaction trajectories to pinpoint limiting process variables. Sensitivity analysis identifies parameters with the greatest influence on performance, while comparisons between predicted and experimental results reveal missing mechanisms or model deficiencies. Simulated reaction profiles can also highlight issues such as nucleotide depletion or suboptimal magnesium-to-nucleotide ratios before they become significant manufacturing challenges.

“Together, these tools provide a data-driven, mechanistic approach to quickly diagnose constraints and guide targeted process optimization,” Xie explains.

The approach also offers significant advantages during scale-up, one of the most challenging phases of bioprocess development. Because the model is grounded in fundamental molecular reactions and biochemical mechanisms rather than empirical correlations, it maintains predictive capability across different manufacturing scales and can be readily applied to new mRNA sequences, all without extensive redevelopment.

Xie says the framework supports predictive design of scale-dependent control strategies, including dynamic pH regulation and fed-batch nucleotide feeding schemes, helping manufacturers reduce development timelines while improving process robustness during technology transfer.

“A key advantage of the modular architecture is its flexibility and interoperability” Xie says. “New enzymes, reagents, or process steps can be incorporated by simply updating or adding the relevant module, without recalibrating the entire model. The framework’s ability to accommodate heterogeneous datasets generated under varying process conditions further supports rapid evaluation of manufacturing innovations while maintaining model consistency.

Perhaps the greatest impact of Xie’s approach lies in advancing quality-by-design (QbD). Acting as an in silico development platform, the modular model enables researchers to evaluate process variables before entering the laboratory. Coupled with digital twin-based Bayesian optimization, the platform narrows the experimental search space, reducing trial-and-error studies while conserving expensive reagents, such as T7 RNA polymerase.

As mRNA pipelines continue to expand beyond vaccines into broader therapeutic applications, modular mechanistic modeling is emerging as a valuable digital bioprocessing tool, enabling manufacturers to accelerate development, strengthen process understanding, and deliver more consistent product quality with fewer experimental resources.

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Free-Floating Bioelectronic Sensors for Fermentation Monitoring

The static, mounted monitoring systems currently used inside fermentation vats are poised to be replaced in the near future with a network of free-floating bioelectronic sensors, if the vision of researchers from Boston University and Capra Biosciences reaches fruition.

Designed for both vat and continuous bioprocessing systems, this bioelectronic sensor network could, ideally “provide spatial information about where they are in a heterogeneous bioreactor platform…as well as multiple measurements of things such as temperature, pH, dissolved oxygen, and dissolved carbon dioxide,” Rabia Yazicigil, PhD, associate professor, Boston University (BU), and lead principal investigator for this project, tells GEN.

Consequently, the network will report data that enable biomanufacturers to determine whether the solution is mixing properly and to identify transit times throughout the process, in addition to specific processing parameters.

“The key innovation…is that these systems integrate living cells into the electronics,” Miguel Jimenez, PhD, assistant professor, BU, emphasizes. The inclusion of microbes—bacteria or yeast cells, for example—“supercharges the sensors,” enabling them to monitor more parameters that are directly relevant to biomanufacturing.

Roughly the size of a chickpea, these sensors never leave the bioreactor. “That allows us to get measurements throughout the reactor… which helps us build a really rich data set that we can then feed into models to help us monitor and predict performance,” notes Mark Poole, PhD, senior director of manufacturing and applied AI, Capra Biosciences.

Paradigm-shifting potential

“Having lots of high-quality measurements at different points in the reactor is game-changing for any biomanufacturing company,” Poole says.

Jon Valdez, program manager at BioMADE, which funded the project as part of a $21.4 million investment in 14 projects to advance the bioindustrial manufacturing industry, agrees, calling it potentially paradigm-shifting. Potential applications extend to clinical monitoring—where a prior collaboration focused on human gut monitoring. The technology is solvent-agnostic but may be most effective in a water-based environment, enabling applications that may include soil and water quality monitoring. Benefits, he says, include lower costs per sensor (estimated at $10−$100) and decreased risk of contamination.

The project is two-tiered. The first tier, the electronics-only sensor, is the nearest to commercialization. Industrial-scale testing will be conducted soon at Capra facilities. “That [alone] would signify a big advance,” Jimenez says, citing the ability to field networked sensors capable of measuring multiple conditions throughout a bioreactor or continuous production process.

The second tier adds the bio component to those sensors. This feature is in academic development. Primary challenges are how to design biohybrid sensors that can be autoclaved or cleaned-in-place, and strategies to stabilize and encapsulate the microbes to be compatible with industrial requirements. The researchers are considering possible approaches now.

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Blood Test Foresees Decline into Alzheimer’s Disease

A blood-based biomarker could predict a person’s risk of developing Alzheimer’s disease years before any symptoms arise, research suggests.

Plasma levels of phosphorylated tau 217 (p‑tau217) may one day help identify at-risk individuals before overt signs of dementia, enabling the pre-emptive use of disease-modifying therapies.

Higher plasma p‑tau217 levels were associated with a greater risk of progressing to cognitive impairment in previously unaffected older adults, and they also predicted faster levels of decline.

The research findings appear in JAMA and were simultaneously presented this week at the annual Alzheimer’s Association International Conference in London.

“In this longitudinal study of several selected cohorts, plasma p-tau217 provided long-term prognostic information for individuals who were cognitively unimpaired at baseline, laying the groundwork for possible future development of individualized risk prediction scores,” proposed Rachel Buckley, PhD, from Mass General Brigham, and co-workers in their published work.

“By providing absolute risk estimates of progression to cognitive impairment, this article moves the field closer to presymptomatic risk stratification with p-tau217, supporting trial design.”

The large, pooled multicohort study included 2684 cognitively unimpaired older adults from six longitudinal studies, who were followed for a median of 5.4 years. Their median age was just short of 70 years, and 63% were women.

Results showed that higher baseline p-tau217 was significantly associated with an increased risk of progression to cognitive impairment during up to 13.5 years of follow up, with a hazard ratio of 1.38 per standard deviation (SD) increase.

This remained significant after accounting for age, sex, education, apolipoprotein E ε4 status, cohort, as well as amyloid positron emission tomography—known to accurately detect Alzheimer’s disease brain pathology.

The researchers report that the absolute risk of cognitive decline at five years was “meaningfully elevated” in the group with very high p-tau217 levels (≥2.5 SD) at 38%, versus just 12% in group with low levels.

Estimated 10-years risks were substantially higher at between 40% and 78% for the low and high p-tau217 groups, respectively. However, just 139 participants—or one in every 20—were followed up for at least a decade and the researchers say these risk estimates should be treated with caution.

Elevated p-tau217 was also associated with faster decline on the harmonized latent Preclinical Alzheimer Cognitive Composite assessment tool.

In an editorial accompanying the published study, Suzanne Schindler, PhD, from Washington University in St Louis school of medicine, and David Wolk, MD, from the University of Pennsylvania, note that cognitive impairment likely reflected multiple etiologies, not just Alzheimer’s disease.

“Indeed, even the low p-tau217 group, in which Alzheimer disease pathology was minimal or absent, still had a 12% risk of progression to cognitive impairment at five years, suggesting the importance of other drivers of cognitive decline in this population and the likelihood that in the higher p-tau217 groups, some proportion of individuals who declined may have primarily been driven by other processes,” they pointed out.

“Notably, discordance between plasma p-tau217 and amyloid PET (especially at intermediate or low amyloid levels) highlights that biomarkers beyond p-tau217 could further improve risk prediction.”

Nonetheless, overall they summarized: “The study by Buckley et al. represents a significant advance. It demonstrates that plasma p-tau217 can provide a time-specific absolute risk estimate for development of cognitive impairment.”

The post Blood Test Foresees Decline into Alzheimer’s Disease appeared first on Inside Precision Medicine.

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AI Could Give CGT Sector Deeper Manufacturing Insights and Greater Control

AI could help cell and gene therapy manufacturers gain a deeper understanding of the complex production processes used to make their products and predict problems before they occur.

A team led by researchers at Northeastern University College of Science in Boston made the case for AI use in a recent paper, arguing that the variability inherent in cell and gene therapy production can be difficult to manage using conventional tech.

Lead author, Jared Auclair, PhD, dean of the College of Professional Studies at Northeastern, tells GEN, “Unlike monoclonal antibodies or recombinant proteins, cell and gene therapies are living or highly complex biological products, making them inherently more variable and difficult to manufacture consistently.

“Every step, from sourcing starting material to manufacturing, analytical testing, storage, and delivery, can influence the final product,” he adds.

Understanding complex, multi-parameter interactions is exactly the sort of challenge at which AI excels, Auclair says, citing the ability to identify critical process attributes as an example.

“AI has the potential to transform cell and gene therapy manufacturing by moving from reactive to predictive manufacturing. Machine learning can optimize process parameters, predict batch failures before they occur, enable digital twins to simulate manufacturing changes, and strengthen quality control through real-time monitoring and anomaly detection,” he adds.

“At Northeastern, our research at the intersection of the Bioanalytical Training Laboratory (BATL), the Center for Bioinnovation and Regulatory Sciences, and AI is exploring how AI can accelerate the development, manufacturing, and regulation of advanced therapies,” Auclair says.

Not plug-and-play

AI’s potential to spot patterns in data is attractive.

However, biopharmaceutical companies looking to adopt the technology are likely to encounter challenges, according to Auclair, who cautions that setting up an AI-driven manufacturing operation is about more than simply buying the right software.

“The technology is advancing rapidly, but successful implementation depends on having high-quality, well-curated data, digital manufacturing infrastructure, and multidisciplinary expertise spanning biology, engineering, data science, and regulatory science.

“AI is not a plug-and-play solution; organizations must build integrated data ecosystems and governance frameworks that regulators can trust,” Auclair says.

AI adoption is a multidisciplinary challenge and should involve people with expertise in all parts of drug development and production, according to study co-author Rominder Singh, PhD, professor of practice, regulatory sciences, & AI at Northeastern.

“Research conducted through the BATL and the Center for Bioinnovation, led by Professor Auclair, has focused on addressing many of these scientific and manufacturing challenges that are unique to advanced therapies.

“This is precisely why Northeastern’s pioneering work in RegSciAI is so important: it brings together regulatory science and AI to ensure these technologies are both innovative and deployable in real-world biomanufacturing,” Singh says.

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In vivo CAR T Industry Leaps Forward with Challenges Ahead

In vivo CAR T sounds like the existing class of Chimeric Antigen Receptor (CAR) T-cell therapies used to provide often individualized treatment for cancer. But they’re a new and emerging class of therapeutics with their own challenges and opportunities for manufacturers.

That’s according to Mo Heidaran, PhD, chief scientist at Cellx, who is due to give a talk at the upcoming Bioprocessing Summit in Boston.

“Whether something is a cell or gene therapy, from a regulatory perspective, depends on [the nature of] the product that’s administered to the patient,” Heidaran explains.

“And, in the United States, in vivo CAR Ts are gene therapy products and not cell therapies as some people talk about them.”

The better-known CAR T products are ex vivo, delivered via modification of patient cells, he explains. Whereas this emerging class of therapies involves delivery of a genetically engineered virus or lipid nanoparticle (LNP) that, in some cases, is stably integrated into the patient genome.

According to Heidaran, the risk of integration is higher when viruses are used.

“My colleagues at the FDA want to make sure people understand it’s very important these products must be [designed] to be very specific to the cell type, perhaps based on data about [some of these] therapies having off-target effects,” he says.

Most in vivo CAR T-cell therapies are in very early stages, with none currently approved for patients, although Heidaran says they are increasingly under investigation by larger companies since they are scalable for a wider range of patients. Also, they are believed to be more cost-effective and have similar logistics, as they don’t require lymphodepletion, he adds.

“Essentially the value driver is that you’re pharmaceuticalizing cell and gene therapy since it’s just a vial of the virus or LNP that you can use to treat many patients—almost like a drug or pill,” he says.

Among the challenges for this emerging class is that several ex vivo CAR T-cell therapies are already approved for patients. In vivo CAR T therapies treat some of the same indications, i.e., certain cancers and autoimmune diseases, he says.

“At some point there has to be a decision made by the FDA about how these [new] therapies compare, such as [running] a study or external control as to whether they’re superior or non-inferior to the same or similar approved ex vivo CAR T,” he says.

Other challenges facing this new industry are about batch sizes for manufacturing, as the equipment and processes for treating ten patients are different from needing to treat thousands. Also, he says, in vivo CAR T therapies need to be monitored to look for off-target effects, durability of response, or an immune response by the patient.

“Overall, to develop a safety profile, we need to define what the effective dose is that people are working to, as these therapies may require repeat administration, which is not done with ex vivo-generated CAR T,” he says.

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Machine Learning Simplifies Accurate LDL Cholesterol Calculation

Researchers at Johns Hopkins University have developed a machine learning-based version of the widely used Martin-Hopkins equation that simplifies the calculation of low-density lipoprotein cholesterol (LDL-C) without compromising accuracy.

The new approach, which is published in JAMA Cardiology, could make it easier for laboratories to estimate LDL-C, improving treatment decisions for patients at risk of cardiovascular disease.

“We’ve optimized the calculation of LDL cholesterol and made this equation accessible and easier for all labs to implement,” said Seth Martin, MD, MHS, senior study author and director of the Advanced Lipid Disorders Program and Digital Health Lab at the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease. “Our goal is to enable clinicians and patients to make better decisions about starting treatments that prevent heart attacks and strokes, and save lives.”

LDL-C is a major cause of atherosclerotic cardiovascular disease (ASCVD) and a primary treatment target. Current guidelines recommend using LDL-C cut offs, such as 70 mg/dL or 55 mg/dL (to convert to mmol/L multiply by 0.0259) in patients with ASCVD, to guide clinical lipid management.

The gold standard for measuring LDL-C concentration is preparative ultracentrifugation but this method is expensive and time-consuming. LDL-C concentrations are therefore usually estimated in routine practice.

One of the most accurate ways to estimate LDL-C concentration is the Martin-Hopkins method, which is recommended for clinical use in the U.S., Europe, and South America. However, implementation can be difficult because it requires users to look up an adjustable factor in a large table that is based on the patient’s triglyceride and non-high-density lipoprotein cholesterol levels.

“A lipid profile with low cholesterol and high triglycerides is the ultimate stress test of the LDL cholesterol calculation,” said Martin. He explains that a 5, 10 or 20 mg/dL difference, based on various equations, could change a person’s eligibility for treatment, such as with PCSK9 inhibitors, which have been shown to significantly lower LDL cholesterol levels. “It’s these types of on-the-cusp examples that benefit most from more accurate results,” he added.

To overcome this barrier and facilitate implementation, Martin and team used a transparent machine learning approach—multivariate adaptive regression splines—to create a simplified, formula-based LDL-C equation.

They trained and tested the tool on data from 4,939,528 adults and children (mean age, 56 years; 53% women) with complete lipid panel test results. These samples, which are representative of the U.S. population, had a median LDL cholesterol level of 114 mg/dL and came from the Very Large Database of Lipids.

The researchers report in JAMA Cardiology that the machine-learning version of the Martin-Hopkins equation estimated LDL-C concentrations that were similar to the original equation, with a minimal difference of 0.5 mg/dL.

Both Martin-Hopkins equations classified 90% of samples within the correct treatment category. Among other commonly used tools for LDL-C estimation, the Sampson-NIH equation correctly classified 86%, the modified Sampson-NIH equation classified 85%, and the Friedewald equation classified 83% in the correct category.

Importantly, said Martin, the investigators found that the Martin-Hopkins equations were the most accurate for classifying high-risk patients with lower ranges of LDL cholesterol levels.

When it came to assessing people who had triglyceride levels between 200 mg/dL and 399 mg/dL and LDL cholesterol levels less than 70 mg/dL, the Martin-Hopkins machine learning equation accurately classified 84% of high-risk samples, the original Martin-Hopkins equation classified 83%, the modified Sampson-NIH equation classified 72%, the Sampson-NIH equation classified 61%, and the Friedewald equation classified 40%.

Martin and co-authors conclude: “Given its high accuracy and straightforward implementation as a single line of code in laboratory information systems, [the Martin-Hopkins machine learning equation] is an alternative option to consider implementing in practice.”

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<![CDATA[Explore how dopamine D2 blockade shapes antipsychotic benefits and risks, revealing dosing pitfalls, polypharmacy harms, and why plasma level monitoring improves outcomes.]]>
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Chronic Pancreatitis Therapies Informed by Patient-Derived Organoids

Approximately three million people worldwide struggle with chronic pancreatitis, for which there is no cure. In a study published in Cell Stem Cell titled “Patient-derived organoids reveal ductal dysfunction and CFTR-modulator responses in chronic pancreatitis,” researchers from Salk Institute have developed an organoid platform to uncover the mechanism of chronic pancreatitis development and identify possible therapeutic strategies.  

The authors generated 37 organoids from patients who developed chronic pancreatitis spontaneously. The organoids revealed consistent dysfunction in the protein cystic fibrosis transmembrane conductance regulator (CFTR), which was identified as a therapeutic target. 

“Though patients can have the same clinical diagnosis of chronic pancreatitis, they can have very different underlying molecular drivers of that disease, which makes treatment especially difficult,” said Dannielle Engle, PhD, assistant professor at Salk and corresponding author of the study. “Our work breaks down a major barrier in the field by establishing an experimental model that preserves patient-specific disease biology and can be used to develop tailored therapies.” 

Over the last decade, organoids have become a prevalent tool to bridge the gap between cell and human studies. Each organoid typically begins with stem or progenitor cells from patients. In Engle’s lab, donor pancreas tissues were used to create miniature replicas of the pancreas. Findings based on a patient’s personalized organoid model could improve therapeutic effectiveness. 

“By growing organoids directly from patients, we preserve key features of ductal cells and ask which disease mechanisms are active in each individual patient,” said Victoria Osorio-Vasquez, PhD, a postdoctoral researcher in Engle’s lab and first author of the study. 

The researchers surveyed the molecular signatures in each organoid and found three subtypes of chronic pancreatitis. This biology-based patient stratification can inform optimal treatment. Results showed that approximately half of the organoids demonstrated dysfunctional CFTR. 

“And CFTR dysfunction was not limited to patients with inherited CFTR mutations, suggesting that functional testing may identify therapeutic opportunities that would be missed by genetic testing alone,” Osorio-Vasquez says. 

Existing CFTR modulator therapies treat patients with cystic fibrosis. The findings suggest that these same therapies may offer pancreatic benefits. The researchers tested clinically available CFTR modulators and found that these therapies could stabilize or restore CFTR function and reduce inflammatory signaling in responsive pancreas organoids. 

The platform also revealed rare alterations to genes, KRAS and TP53, in some chronic pancreatitis organoids, supporting future use of the system to study disease evolution, pancreatic cancer risk, and biomarker discovery at the interface of chronic inflammation and pancreatic cancer. 

“These organoids gave us a way to study chronic pancreatitis pathogenesis in human cells for the first time,” says Engle. “Our platform enables a more personalized way of studying and eventually treating chronic pancreatitis, while also blazing the trail for other organoid-based platforms in other inflammatory disease contexts.” 

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