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Veracyte Launches Test Identifying Breast Cancer Patients Who Don’t Need Chemo

Buoyed by the remarkable results of the OPTIMA trial presented at the ASCO Annual Meeting, Veracyte this week announced the U.S. launch of the Prosigna Breast Risk of Recurrence (ROR) test, a genomic test for patients diagnosed with early-stage hormone-receptor positive (HR+) breast cancer. 

“Prosigna is the only test proven to be predictive for chemotherapy benefit in a Phase III prospective trial for patients with up to nine positive lymph nodes, including premenopausal women, all of whom were recommended to receive ovarian suppression,” Phillip G. Febbo, MD, chief scientific and medical officer at Veracyte, told Inside Precision Medicine. 

The Prosigna test determines a patient’s ROR score and estimates the 10-year probability of distant recurrence. It thus help guide treatment decisions, including predicting whether high risk patients are likely to benefit from chemotherapy or may safely achieve optimal outcomes with endocrine therapy alone, helping clinicians to make personalized care plans for their patients. 

Prosigna will be available to order starting June 8, 2026.

“Every patient diagnosed with breast cancer deserves answers they can trust about what their cancer means and what comes next,” said John Leite, PhD, chief commercial officer, Veracyte​.

“Prosigna gives patients and their oncologists a deeper understanding of their individual risk of recurrence, and, for many, whether chemotherapy will truly benefit them or whether they can safely avoid it. That kind of personalized insight can bring greater confidence and reassurance as patients navigate decisions that will shape their care and future health.”

In the U.S., more than 225,000 new HR+/HER2- breast cancer cases are diagnosed each year. When breast cancer is diagnosed early and treated appropriately, five-year survival rates reach 92%.

“For decades, clinical staging just comprised tumor size and nodal status together with pathology. This was the standard approach to determining whether patients with HR+/HER2- breast cancer need chemotherapy. Many node-positive patients routinely receive chemotherapy as standard of care,” Febbo said.

He added, “And yet, chemotherapy is likely to only benefit a relatively small fraction of these patients.” 

The OPTIMA trial uses the Prosigna test to guide therapy and testing. Those patients with a low Prosigna ROR (<60) can safely avoid chemotherapy. OPTIMA shows that tumor biology, as evaluated by the Prosigna test, provides information beyond traditional clinical factors.

Prosigna is based on the PAM50 genomic classifier, which classifies tumors into four intrinsic subtypes. Prosigna, the company said in a press release, “uniquely combines intrinsic subtypes and proliferation score with clinical pathological factors into a single comprehensive analysis to calculate a patient’s Risk of Recurrence (ROR) score and predict 10-year probability of distant recurrence.”  

Veracyte’s portfolio includes multiple cancer types, including tests for prostate cancer (Decipher Prostate), bladder cancer (Decipher Bladder and TrueMRD for muscle invasive bladder cancer monitoring), and others. The company said, “Prosigna’s intrinsic subtyping data positions us well for future expansion. While ROR scoring is specific to HR+/HER2- disease, the underlying intrinsic subtyping information spans all breast cancer types.” 

More than 225,000 new HR+/HER2- breast cancer cases are diagnosed annually, with approximately 75,000 patients having node-positive disease annually. The OPTIMA trial demonstrated that more than two-thirds of clinically high-risk patients (68%) can safely avoid chemotherapy—a significant population previously assumed to require treatment based solely on nodal status. 

The post Veracyte Launches Test Identifying Breast Cancer Patients Who Don’t Need Chemo appeared first on Inside Precision Medicine.

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Nanotube Sensor Provides Chemical Imaging for Earlier Detection of Bladder Cancer Recurrence

Researchers at the Massachusetts Institute of Technology (MIT) have developed a catheter-based nanosensor that can detect the recurrence of bladder cancer at an earlier stage by identifying a biomarker released by tumors directly within the bladder. The technology, detailed in Nature Nanotechnology, combines carbon nanotube sensors with a rotating optical imaging device to create what researchers describe as a three-dimensional chemical imaging platform capable of locating cancer-associated biomarkers within tissue.

“It’s like a camera for molecules instead of light,” said Michael Strano, PhD, a professor of chemical engineering at MIT and senior author of the study. “If you have a billion nanosensors in an array, you can use them to make a chemical image that helps you locate their source.”

Roughly 85,000 people in the U.S. are diagnosed with bladder cancer each year, and more than half of patients experience recurrence within five years. The disease also carries among the highest lifetime treatment costs because patients require ongoing surveillance.

The new method addresses a major challenge in detecting bladder cancer recurrence, which currently relies on urinalysis to look for signs cancer has returned. Using urine for detection is hindered since biomarkers of cancer recurrence are diluted, degraded, and are often cleared shortly after they are released by the new tumor.

To address this, the MIT researchers targeted the matrix protein 22 (NMP-22), an FDA-approved biomarker for bladder cancer. While it is possible to detect NMP-22 in urine, the shortcomings of that method often mean that the cancer is often only found at a later stage of development.

To overcome this gap, the MIT team looked for a way to develop a sensor that could work inside the bladder. Their device consists of a urinary catheter coated with specialized nanosensors that detect the presence of NMP-22. A miniature rotating ball lens embedded in the catheter tip emits laser light and collects fluorescent signals generated by the nanotubes when they interact with the target biomarker.

Over the past decade, Strano’s laboratory has focused on developing carbon nanotube-based sensors that can identify specific molecules. The team customizes nanotubes for each application by applying different polymer coatings that function as “synthetic antibodies,” enabling them to bind to disease biomarkers. Previous development efforts in the lab have yielded sensors capable of detecting molecules such as hydrogen peroxide, riboflavin, and viral proteins.

For the bladder cancer application, researchers engineered a phospholipid-containing a synthetic polymer that selectively recognizes NMP-22. The project was partly motivated by earlier computational modeling conducted by the team, which suggested that placing sensors near an emerging bladder tumor could produce “a more than 50,000-fold increase in detection limit by minimizing biomarker dilution and degradation.” That finding pointed toward the potential advantages of measuring biomarkers at their source rather than after they enter urine.

In experimental testing, the platform achieved a 182-fold signal enhancement compared with biofluid sampling. The new technology was tested in multiple ways. The researchers first tested the nanosensors against six bladder cancer cell lines and healthy fibroblast cells and found distinct sensor responses associated with cancer-cell apoptosis. The team also used the chemotherapy drug gemcitabine to induce cancer cell death in vitro and monitored the resulting release of biomarkers. siRNA analysis confirmed that NMP-22 was a major contributor to the detected signals.

Researchers then evaluated the platform in porcine bladder models using a catheter coated with the specially designed nanotubes. Using a rotating ball lens system, they generated chemical maps showing the location of biomarker sources and demonstrated detection at distances up to two centimeters from the source. The researchers also reported a simulated tumor resolution of 16 square millimeters.

The chemical imaging capability is a unique attribute of the new method and the information it can provide should be very useful for treating recurrent cancer. “If you are scanning over a region of tissue, you would like to know not just that there is a signal indicating that a tumor is there, but also its location so that you can treat it or perform a biopsy,” Strano said. “Before an early-stage tumor breaks through the urothelium so that it’s visible, it’s under the surface but still emitting chemical signals that can be imaged.”

While the study focused on bladder cancer, the researchers indicated that the underlying platform may have broader applications. By replacing the nanosensors with versions designed to recognize other biomarkers, the system could potentially be adapted for additional cancers or for diseases affecting the cardiovascular or gastrointestinal systems.

“The beauty of polymer chemistry is that if we understand the molecular structures of target biomarkers and the design principles of binding sites, we can develop new sensors tailored to different diseases,” said lead author Wonjun Yim, PhD.

Future work will focus on expanding the range of detectable analytes, developing multiplexed sensor arrays, conducting omics-based biomarker validation studies, and improving optical engineering. The researchers also aim to miniaturize the imaging system and integrate the technology into cystoscopes already used in clinical practice.

The post Nanotube Sensor Provides Chemical Imaging for Earlier Detection of Bladder Cancer Recurrence appeared first on Inside Precision Medicine.

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Feasibility of Technology-Assisted Lifestyle Self-Monitoring in Older Adults With Type 2 Diabetes: Mixed Methods Pilot Study

Background: Type 2 diabetes is a major public health concern in older adults. Healthy lifestyles, such as physical activity and healthy eating, are effective strategies for diabetes self-management. Increasing evidence shows that health technologies can promote healthy lifestyles for diabetes management. However, limited research has evaluated their use among older adults with type 2 diabetes. Objective: This study evaluated the feasibility and preliminary health outcomes of technology-assisted lifestyle monitoring for diabetes management in older adults with type 2 diabetes. The study also examined participants’ experiences to identify barriers and facilitators to sustained technology-assisted lifestyle modification. Methods: This 12-week pilot study used a pretest and posttest design. Feasibility was assessed by recruitment, retention, and adherence to device-based self-monitoring, including the percentage of days with tracked steps (PDWTs) and the percentage of days with food logs (PDWFLs). Fitbit fitness trackers paired with smartphone apps were used to track physical activity and food intake in 15 overweight/obese older adults with type 2 diabetes (mean age 70.5, SD 4.8 y). Self-monitoring behaviors were tracked throughout the study. Baseline and 12-week health outcomes (eg, hemoglobin A [HbA] and physical function) were compared using paired 2-tailed tests or Wilcoxon signed rank tests, as appropriate; effect sizes were calculated using Cohen . Associations between self-monitoring data (PDWTs, average daily steps, PDWFL) and health outcomes were examined using Pearson or Spearman correlations. Semistructured interviews were conducted at the study completion, and thematic analysis was used to analyze qualitative data. Results: The target sample (n=15) was successfully enrolled over 6 months, with 100% retention at 12 weeks. Feasibility was supported by consistent use of wearable devices for self-monitoring of physical activity, although dietary logging adherence varied. HbA decreased from baseline to 12 weeks (effect size −0.49, 95% CI –1.15 to –0.04; =.04), and PDWFL was inversely correlated with HbA (=−0.53; =.04) at follow-up. The qualitative findings indicated that barriers and facilitators to technology-assisted lifestyle self-monitoring and diabetes management through lifestyle modifications exist across multiple levels, including the individual, interpersonal, organizational or community, and societal levels in older adults with type 2 diabetes. Factors at one level interacted with those at other levels. For example, limited technological proficiency challenged lifestyle tracking, while interpersonal and organizational support helped mitigate barriers. Conclusions: Technology-assisted self-monitoring of lifestyle behaviors was feasible in this sample of older adults with type 2 diabetes and was associated with favorable signals in glycemic control. While causal inferences cannot be drawn from this single-arm pilot study, observed within-subject changes and behavioral-clinical correlations support further evaluation of technology-assisted lifestyle self-monitoring. The findings also highlight the importance of addressing interconnected factors at multiple levels, tailored to older adults’ unique needs and capacities in diabetes self-management.
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Implementation of an e-Tool (the Provider Asthma Assessment Form) Integrated Into the Electronic Medical Record in Primary Care: Mixed Methods Survey of Perceived Utility, Practitioner Satisfaction, Barriers and Enablers

Background: Asthma care gaps between best practice and clinical practice contribute to the burden of asthma on individuals and society. Electronic medical records (EMRs) provide a unique opportunity to integrate novel e-tools at the point of care. Objective: The purpose of this study was to evaluate the perceived utility, health care practitioner satisfaction, and barriers and enablers associated with the implementation of the Provider Asthma Assessment Form (PAAF), a novel e-tool integrated into the primary care EMR. Methods: Health care practitioners (n=80) at a family health team were invited by email to participate in a voluntary survey regarding their use of the PAAF in their role within the family health team. Respondents who had used the PAAF were asked to assess its perceived utility, their satisfaction with it, and any associated barriers and enablers. Responses were analyzed using descriptive quantitative analysis and qualitative analysis to identify major themes. Results: In total, 18 responses were included, including 4 (22.2%) from practitioners who had used the form and 12 (66.7%) from practitioners who had not. Overall, most practitioners who used the form were satisfied with the PAAF and reported that it was helpful in clinical practice, aided decision-making, and was user-friendly. Enablers such as detailed documentation, decision support, and multidisciplinary involvement were identified. Several barriers were also identified, including time constraints, lack of knowledge and training regarding the use of the form, limited opportunity to use it, and limited availability of necessary data elements to complete the PAAF. Conclusions: The PAAF was perceived to be a useful and largely satisfactory e-tool by responding practitioners. However, several barriers limited user uptake and sustained use. Future directions for PAAF implementation include increased tailoring to the needs of primary care teams and leveraging technological advancements. Lessons learned from the PAAF can inform the development and implementation of novel e-tools in primary care EMRs.
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How virtual power plants could provide energy for data centers

Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center?

Google just signed a new deal to help pay for a virtual power plant (VPP) in the largest power grid in the US. The agreement is with Voltus, a leading VPP and distributed energy resources platform.

Voltus will set up the virtual power plant, grouping together devices like electric vehicles and smart thermostats. It’ll pay customers to participate, and the company will dial back power or use the stored energy during times when the grid is stressed. Google will foot the bill for setting it up, and the extra capacity generated by the project will help run its data centers in the region.

This is one of the most concrete examples so far of a tech giant using a VPP to help meet energy demand for data centers. But there are still some lingering questions about just how far this sort of program can go, and what the limits are.

Last year, it felt as if everyone was talking about data center flexibility. A high-profile study from Duke University found that if data centers agreed to decrease their energy demand for roughly 40 hours per year, a whole bunch of them (about 100 gigawatts’ worth) could come online without making new power plants or transmission equipment necessary.

The underlying reason is that our power grid is designed not for our average energy use, but for the absolute maximum: the brutally hot July evening when everyone is blasting their air conditioners, watching Love Island, and microwaving popcorn. If a data center is willing to refrain from pulling so much power during those high-stress times, the grid can happily support it the rest of the year.

One lingering question here is about incentives: How would you get data centers to agree to this? After all, they might not have a very flexible load, especially now that AI use is more widespread—training a model can easily be delayed or shifted, but customer demand is more immediate. Giving up computing capacity could mean losing revenue.

Regulation is one approach that could work here. One proposal in the US would allow new data centers to come online years sooner if they agree to lower demand when the grid is nearing its max.  And a new Texas law requires large users to switch to backup power or curtail their demand in emergency situations.

Another approach is for data center operators to pay for other people to be flexible.

Voltus announced a new program in September that allows data centers to finance flexibility on their local grid. The company calls it “Bring your own capacity.” Google is now the first named customer taking advantage of this program.

In the new agreement, Voltus will pay people who agree to participate in the virtual power plant. The plant will be part of PJM, the grid that covers much of the US East Coast. The company says it will be able to aggregate up to 100 megawatts of distributed energy resources each year. The plant should be operational in 2027, according to Voltus.

This isn’t Google’s first foray into flexibility; the company has agreements with utilities across the US to limit or shift its own energy demand, which can help free up grid capacity. As the company pointed out in a blog post earlier this year, though, there are limits on how flexible a data center can be, and not every facility will be able to ramp down its power demand.

“There is no one solution for expanding grid capacity and we’re continuing to explore all options, including the many avenues for load flexibility,” said Michael Terrell, Google’s global head of advanced energy, in an emailed statement in response to written questions.

Once again, I’m wondering about incentives here. These companies are asking homes and businesses to be flexible. Will they agree?

A recent study in California looked at local people’s willingness to participate in managed electric-vehicle charging. Essentially, the program pays people to give up control of when they charge their EVs. This is another way to help smooth out electricity demand and ease the burden on the grid.

The problem? Not many people signed up. With no economic incentive, only 1% of EV owners enrolled in managed charging. At $40 per month (about 15% of their power bill), only 4.6% did.

This is a different situation and a different region from the one in which Google is working with Voltus. (It’s worth noting that the companies aren’t sharing how much they plan to pay the participants, which will obviously be a big determinant in participation for this kind of project.) 

But this study shows that even with money on the table, people may not always jump at the chance to cede control of their electricity demand. And it certainly feels relevant that about 70% of Americans oppose AI data centers in their area, according to recent Gallup polling. 

Being flexible sounds like a great idea in theory, and these financed VPPs could provide an immediate route to meeting energy demand. But as we move from idea to implementation, it’ll be interesting to see whether trial runs work as intended.  

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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AI Model Predicts Radiation Uptake Before Treatment in Advanced Prostate Cancer

A machine learning-based model that integrates imaging uptake features, radiomics, and biomarkers accurately predicts how much radiation is absorbed by patients undergoing prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC).

“One of the biggest challenges in radioligand therapy is that patients can receive very different radiation doses despite being prescribed the same treatment activity,” said Amit Nautiyal, PhD, scientist and National Institute for Health and Care Research fellow at University Hospital Southampton and the University of Southampton in the U.K.

“Our findings suggest that information already available before treatment, such as 18F-PSMA PET/CT imaging and routine clinical biomarkers, may help predict how radiation will be distributed within tumors and healthy organs.”

Nautiyal told Inside Precision Medicine that, in the future, the model “could support more personalized treatment planning, helping to maximize radiation delivery to tumors while minimizing unnecessary radiation exposure to healthy tissues. Ultimately, the goal is to improve treatment effectiveness while reducing the risk of side effects.”

At present, the only way to determine how much radiation has been absorbed by the tumor and surrounding organs such as the kidneys and salivary glands is to use post-treatment imaging and dosimetry calculations, which can be time-consuming and resource intensive.

“Our approach aims to use information already available before treatment, such as positron emission tomography/computed tomography (PET/CT) scans and routine clinical data, to estimate likely absorbed doses before therapy begins,” said Nautiyal.

He and his team integrated 18F-PSMA PET/CT uptake data (total lesion uptake, tumor-to-organ ratios), radiomics features (Gray-Level Co-Occurrence Matrix), and biomarker information (estimated glomerular filtration rate) into a machine learning-based hierarchical mixed-effects model to provide pretherapy predictions of absorbed dose in tumors and organs at risk during ¹⁷⁷Lu-PSMA RLT.

The model, which was presented at the Society of Nuclear Medicine and Molecular Imaging 2026 Annual Meeting, incorporated data from nine patients with mCRPC referred for ¹⁷⁷Lu-PSMA RLT, contributing 57 tumors, 36 salivary glands, and 18 kidneys for analysis.

At the end of cycle 1, ¹⁷⁷Lu-PSMA dosimetry showed that the mean absorbed dose was 11.0 Gy for tumors, 1.8 Gy for salivary glands, and 3.9 Gy for kidneys.

For tumors, the models achieved a mean absolute error (MAE) of 3.2 Gy for the prediction of absorbed dose, meaning that, on average, the predicted tumor dose differed from the measured dose by approximately 3.2 Gy.

By comparison, the MAE was 0.3 Gy for salivary glands and 0.1 Gy for kidneys.

“Given the biological complexity of metastatic prostate cancer and the relatively small study cohort, we consider this an encouraging result,” said Nautiyal, “Tumor dose prediction is inherently challenging because different tumor lesions can behave quite differently, even within the same patient. By contrast, organs such as the kidneys and salivary glands generally exhibit more consistent uptake patterns, which likely contributed to the higher predictive accuracy observed.”

The Bayesian R² values, which indicate how much of the variation in absorbed dose can be explained by the model, were 0.73 for tumors, 0.93 for salivary glands, and 0.99 for kidneys.

The researchers also calculated the 95% Highest Density Interval (HDI) for the model, which indicates whether the uncertainty estimates produced by the model are realistic. The HDIs were 0.89, 1, and 1, for tumors, salivary glands and kidneys, respectively, meaning that, for tumors, about 89% of observed absorbed doses fell within the range predicted by the model.

“This suggests that the model is not only making reasonable predictions but is also providing realistic estimates of how confident it is in those predictions, said Nautiyal. “This is particularly important in healthcare, where understanding uncertainty is often as important as the prediction itself.”

The researchers say that, taken together, the findings support the robustness of the model. They also carried out a leave-one-patient-out analysis, which showed that performance remained stable even when individual patients were excluded from model development and then used for testing.

“This suggests that the model is learning broader patterns rather than simply memorizing the training data,” noted Nautiyal.

Although the results are promising, the researchers acknowledge that this was an early proof-of-concept study and further work is needed before the model can be used routinely in clinical practice.

They now plan to evaluate the model in larger patient populations from multiple centers in the U.K., perform independent external validation, and investigate how predicted absorbed doses correlate with clinical outcomes.

Nautiyal concluded: “If future studies continue to show promising results, predictive tools of this type could eventually support treatment planning and patient stratification in molecular radiotherapy. The aim is to help clinicians make more informed treatment decisions before therapy begins and move towards more personalized radioligand therapy.”

The post AI Model Predicts Radiation Uptake Before Treatment in Advanced Prostate Cancer appeared first on Inside Precision Medicine.

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Bioproduction Pivots from Centralized to Regional Support

The global biopharma industry is placing increasing importance on regional support rather than only centralized expertise to help complex programs advance. A key benefit is access to local expertise in or near their time zone.

The localization movement “is part of a global shift [in which] companies are assessing how they balance cost, quality, and risks across regions rather than relying on any single market,” Jessay Devassy, PhD, global R&D director, Ecolab Life Sciences, tells GEN.

Ecolab opened a bioprocessing applications lab in Korea this spring. “Being in Korea allows the exchange of ideas in an iterative fashion…so knowledge moves seamlessly between regions. That’s much easier if you’re in their proximity,” Devassy points out.

This is the company’s first bioprocessing lab in Asia. Situated in Dongtan, Korea, it supports process development studies from early- to commercial-scale, focusing on biologics’ downstream purification.

Korea was a logical choice. “Korea is highly advanced in manufacturing,” Devassy continues. Now it’s evolving from a manufacturing hub to a comprehensive biopharma ecosystem, with active contributions from R&D all the way through clinical development, with home-grown and multinational companies alike.

With its biologics manufacturing history, “I think Korea has become one of the most trusted locations globally,” he says. “Its quality standards are well-aligned with North American and European standards.” Consequently, global clients are assured that the same approaches and standards are applied to development as in the United States or Europe.

Korea’s aspirations

Government support is part of that. The Korean government designated biopharma as a strategic industry after COVID-19 and reiterated that goal in 2023’s Third Five-year Comprehensive Plan for Development and Support for the Bio-Pharmaceutical Industry. Key points include developing two blockbuster drugs by 2027, doubling pharmaceutical exports to $16 billion, and positioning Korea among the top six nations for pharmaceutical development.

At the end of 2025:

  • New venture capital investments in biotech and medical companies reached $830 million, up approximately 11% from the prior year.
  • Total venture investments in the biotech and medical sector rose more than 29% from 2024, more than for any other industry.
  • Continuous bioprocessing is expected to experience a compound annual growth rate of nearly 20% between 2025 and 2030, reaching revenues exceeding $21 million.

Challenges

The competition to attract biopharma companies is robust. India is the fastest-growing Asia-Pacific market, but, Devassy says, “China has a cost advantage…[in] manufacturing and development.” It’s also the largest biopharma market in the Asia-Pacific region.

“Japan has more established domestic systems for biomanufacturing,” Devassy continues. According to Grand View Horizon, Japan leads the pack for projected revenue from continuous bioprocessing to 2030.

Devassy positions Korea “somewhere in between” China and Japan. “It’s strong technically, but is still navigating regulatory complexity and global competition.” Currently, it generates 2.2% of the world’s continuous bioprocessing revenues.

“Biopharma exports from Korea have seen strong growth recently…and Ecolab is playing a strong part in supporting the manufacturers behind that growth,” Devassy says. “This is our first step toward making our global expertise accessible to growing markets in Asia.”

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Gentler Cell Separation Methods Gain Momentum

The race to commercialize cell therapies is forcing bioprocessing innovators to confront one of the field’s most persistent manufacturing bottlenecks: isolating fragile hematopoietic stem cells (HSCs) without compromising their therapeutic potential. “HSCs are extremely rare and extremely delicate,” says Sophie He, PhD, vice president of cell therapy and head of mergers and acquisitions at Bracco. “Trying to isolate HSCs while preserving their therapeutic function is extremely difficult.”

The challenge begins with biology itself. CD34+ hematopoietic stem and progenitor cells typically account for just one to three percent of mobilized apheresis collections and one to four percent of bone marrow populations, while the most primitive long-term HSCs can represent less than one-tenth of a percent of total marrow cells. That rarity means every processing step matters.

For manufacturers scaling autologous and allogeneic therapies, the result is a difficult balancing act between purity and yield. Conventional enrichment workflows often sacrifice one to achieve the other. “To get higher purity, traditionally one gets lower yield,” He explains. “Every wash or transfer step in the isolation process results in cell loss.” The problem is magnified by the fact that HSCs rely on preserving self-renewal, multipotency, and engraftment capability—functions that can easily be disrupted during processing, ultimately reducing clinical effectiveness.

As developers move toward commercial-scale manufacturing, traditional magnetic separation systems are facing growing scrutiny. According to He, magnetic columns can expose HSCs to damaging shear forces, compression, and membrane stress because of their fragile membranes and cytoskeletons. Processing times can also become a major operational burden. “Magnetic columns can require more than 10 hours to completely process larger mobilized apheresis starting material,” she says. “That could lead to apoptosis and metabolic stress.” The lengthy workflows create additional challenges for scalability and reproducibility across manufacturing sites, particularly as companies transition from small clinical batches to commercial production runs.

Newer approaches are gaining attention for their ability to handle cells more gently while supporting larger-scale workflows. Among them, microbubble-based separation uses buoyancy rather than magnetic force to isolate HSCs. He says the technology reduces mechanical stress on cells while also minimizing concerns about residual materials left behind during processing. The broader industry goal, however, extends beyond replacing one technology with another. Developers are searching for a platform simultaneously capable of delivering high purity, high yield, preserved cell functionality, and proven scalability.

He describes the search for an ideal HSC isolation platform as “the holy grail” for cell-therapy bioprocessing at a commercial scale. In addition to biological performance, future systems must reduce operator dependency, integrate efficiently into manufacturing workflows, and support reproducibility across donors, sites, and operators. Regulatory clarity will also be essential before any technology can achieve widespread adoption. As regenerative medicine advances toward broader commercialization, the ability to isolate healthy stem cells consistently and at scale might determine which therapies successfully transition from experimental promise to industrial reality.

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Web App Helps Flag Antibodies Where Manufacturability Might Be an Issue

Researchers have developed an open-source web app to help drug manufacturers and developers identify unstable antibodies prone to aggregation. The team from Oxford University says the Therapeutic Antibody Profiler 2 (TAP2) can compare the fragment variable component of a proposed antibody to successful clinical-stage antibodies.

According to Clare Gillis, a researcher in bioinformatics and computational biology, the app has the potential to help companies begin process development. “It can help them if they already know their antibody binds as they want, but they need to know if it will pass through the whole developability and manufacturability pipeline,” she says.

TAP2 uses five easily calculable physiochemical metrics based on surface residues of the antibody, Gillis says. These are more likely to affect manufacturability.

The web app metrics are selected to model aspects of antibody behavior, such as hydrophobicity, she adds. If there are big patches of hydrophobic residues on the outside of the antibody, then it’s more likely to be reactive and, thus, less likely to remain stable as a formulated drug product.

Likewise, Gillis explains, if the surface of the antibody features large patches of positive or negative charge, it is likely to have nonspecific reactions that will cause destabilization and aggregation.

With the TAP2 app, companies can flag early amber or red warnings for antibodies where manufacturability might be an issue. In addition, the group also offers a web app profiler for therapeutic nanobodies, TNP, as well as Humatch, an app that can help tweak antibodies to be more ”human-like” and less likely to cause immune reactions in patients, she says.

About Humatch, Gillis says, “you can add a best single point mutation and then iterate over and over until the model believes the antibody is fully humanized.” The app works for any antibody with paired heavy and light chain variable domains (VH and VL), she says, and can potentially help manufacturers of harder-to-produce products that don’t exist in nature.

The post Web App Helps Flag Antibodies Where Manufacturability Might Be an Issue appeared first on GEN – Genetic Engineering and Biotechnology News.

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NIIMBL to Support Vector Production and AI-Ready Training Projects

Viral vector production and training schemes designed to foster development of an AI-ready workforce dominate the list of projects selected for support by the U.S. National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL).

The institute, a public-private partnership focused on advancing manufacturing and solving industry challenges, announced its latest funding awards, explaining that the 39 recipients would support U.S. production and talent development.

Sandeep Kedia, NIIMBL senior technology fellow and project call program lead, says the projects “represent the kind of innovation needed to strengthen the nation’s biopharmaceutical manufacturing capabilities.

“By bringing together advanced process analytical technologies, AI-driven optimization, and next-generation production platforms, our members are helping accelerate the adoption of transformative technologies across the industry,” he adds.

Several of the selected projects focus on the production of adeno-associated viral (AAV) vectors—hollow viruses used to deliver genetic information—which play a crucial role in cell and gene therapy manufacturing.

For example, researchers at Michigan Technological University will work with industry partners on an aqueous two-phase continuous vector purification system. The aim is to boost yield while reducing cost, labor, and analytical complexity.

Similarly, a team at North Carolina State aims to develop “improved purification materials that can better capture full AAVs, along with machine-learning software that identifies optimal process conditions.”

The third vector-focused project will see an MIT group work with EMD Millipore, Landmark Bio, and Virica Biotech to try to reduce the number of empty viral capsids inadvertently made during vector production.

The researchers will combine an approach called decoupled replication-initiated vector encapsulation, or DRIVE, with various process control strategies to create a platform that makes high-titer, high-quality rAAVs.

According to the MIT team, “By reducing [the proportion of] empty capsids, the approach can streamline downstream purification, reduce time and cost, and improve the overall quality of gene therapy products.”

AI-ready workforce

In addition to the technology projects, NIIMBL will support several training programs with an emphasis on ensuring the next generation of biopharmaceutical engineers are AI-ready, according to workforce director John Balchunas.

“Our workforce initiatives are designed to meet talent needs head‑on by creating more innovative pathways into biomanufacturing careers,” he says, adding, “These new projects will strengthen partnerships and ensure that learners can gain the skills needed to thrive in a rapidly evolving biopharma industry.”

One such project will see a team at Texas A&M University’s National Center for Therapeutics Manufacturing expand an existing effort called NeuroPipes, which seeks to interest neurodiverse people in careers in biopharma. The aim is to provide technical skills training that prepares neurodivergent adults for careers in drug manufacturing.

Another project will see Wistar Institute researchers set up BioPATH, a national consortium focused on advancing workforce training in biomanufacturing, AI, and automation.

The idea, according to the Wistar team and collaborators at the International Academy of Automation Engineering, is to “bridge the gap between foundational bioprocess and GMP knowledge and the emerging needs of automation, data-driven manufacturing, and digitally enabled quality systems.”

The post NIIMBL to Support Vector Production and AI-Ready Training Projects appeared first on GEN – Genetic Engineering and Biotechnology News.