Category: Imported News

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Supply Chain Digital Twins: An Evolution, Not a Breakthrough

Digital twins help optimize drug production processes by modeling the thousands of interactions that cells, raw materials, and reagents undergo in culture. And new analysis suggests they could do the same thing for supply chains.

Researchers at the U.S. National Institute of Standards and Technology (NIST) and EMD Millipore put forward the idea, arguing that twins could make drug distribution, which is also characterized by thousands of interactions, more resilient and efficient.

Lead author Perawit Charoenwut, a logistics researcher at NIST’s systems integration division, tells GEN, “A digital twin could be extremely helpful in all phases of the biopharmaceutical supply chain. Starting from demand planning triggered by global events such as pandemics, regional disease outbreaks, aging demographics, etc., through to being able to provide visibility on capacity requirements and limitations.”

In silico models could also provide solutions to disruption by identifying alternative supply options, such as distribution centers or regional inventories, in less time, Charoenwut says.

“Digital twins could also be helpful in evaluating different suppliers by running simulations on their potential performance, based on different demand scenarios versus their individual capacities and capabilities,” he continues.

Standards

In theory, digital twins are a good option for supply chain modeling and management. In practice, however, firms interested in the approach will need to overcome some technical challenges.

For example, one major hurdle is the lack of data standardization, according to study co-author Boonserm Kulvatunyou, PhD, a computer engineer at NIST. “Supply chain digital twins require data from across organizations and third-party sources,” he tells GEN. “The lack of industry standards creates challenges in obtaining all the necessary data.”

With this in mind, the NIST’s Industrial Ontology Foundry (IOF) is working with the National Innovation Institute for Manufacturing Biopharmaceuticals (NIIMBL) to develop open-source ontology and schema standards for connecting data.

Kulvatunyou says, “The aim is to provide a semantic foundation for connecting data and knowledge across the manufacturing and supply chain operations.

“Further work is being conducted to cover broader materials, processes, and quality data,” he says. “We would like to invite industry and academia to join this effort and benefit from these new standards.”

Industry interest

Biopharma firms interested in digital supply chains will also need to establish a solid data infrastructure, according to Charoenwut, who says companies should start small and pace themselves.

“We think that biopharma companies do believe that digital twins could make a significant difference in their supply chain efficiency and resiliency. Many of them are probably building prototypes and proofs-of-concept to demonstrate the value and potential benefits, but then soon realize the digital data foundation gaps that need to be addressed in parallel in order to fully adopt this technology.

“As digital twins can vary in detail and complexity, companies should strategize digital twin adoption by starting with lower-complexity cases based on available digital data and progressively moving up the scale to gain greater precision and new capabilities. In other words, the implementation of digital twins should be viewed as an evolution rather than a breakthrough,” he says.

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Milk Exosomes Transform Therapeutic Bioprocessing

Breast milk has long been understood as more than infant nutrition. It is a biologically active system packed with molecular signals that help shape immune development, metabolism, and even brain function. Among its most intriguing components are milk-derived extracellular vesicles—tiny lipid-bound particles often called milk exosomes—that are rapidly becoming one of bioprocessing’s most promising therapeutic tools.

These nanoscale carriers are naturally designed for transport. They can survive digestion, move into circulation, and distribute cargo throughout the body, with studies suggesting they may even reach the brain during early development. Researchers have shown that these vesicles can influence central nervous system communication, particularly through interactions with microglia, which are crucial to the brain’s immune cells. The ability of milk exosomes to carry microRNAs and regulate epigenetic pathways, including DNA methyltransferase 1 (DNMT1), points to a sophisticated biological delivery system that the industry is now learning to harness.

That potential is especially compelling in drug manufacturing, where delivery often determines whether a therapy succeeds or fails. Traditional nanoparticles can trigger toxicity, instability, or poor absorption. Milk exosomes offer a more elegant alternative: they are biocompatible, naturally abundant, and scalable for pharmaceutical development.

Huiming Tu, MD, a researcher and clinician in the department of gastroenterology at the Affiliated Hospital of Jiangnan University in Wuxi, China, and his colleagues recently demonstrated this with ulcerative colitis. Their team developed an oral delivery platform called mEXOs@TOF, which loads the pan-JAK inhibitor tofacitinib into milk-derived exosomes. The resulting formulation showed strong pharmaceutical performance, including consistent particle size, high drug-loading efficiency, and strong stability during delivery.

More importantly, the therapy improved anti-inflammatory outcomes through multiple mechanisms. It lowered inflammatory mediators such as IL-6, IFN-γ, and nitric oxide, while increasing anti-inflammatory IL-10. It also reduced oxidative stress and suppressed activation of the JAK-STAT3 signaling pathway. In both laboratory and animal studies, the system delivered strong therapeutic benefits without detectable toxicity—an ideal benchmark for translational bioprocessing.

Cancer therapy is seeing similar innovation. Min Suk Shim, PhD, professor of nano-bioengineering at Incheon National University in the Republic of Korea, and colleagues focused on sonodynamic therapy, in which ultrasound activates a sensitizing drug to destroy tumors. Their challenge was improving intracellular delivery of chlorin e6 (Ce6), a common sonosensitizer.

The team engineered glutathione-responsive milk exosomes by incorporating a diselenide bond-bearing fatty amine derivative. This allowed the vesicles to remain stable during circulation but release Ce6 inside breast cancer cells, where glutathione concentrations are higher. Once ultrasound was applied, reactive oxygen species production increased dramatically, leading to significant cancer cell death in MCF-7 breast cancer models. The work shows how responsive bioprocess design can turn natural vesicles into precision-triggered therapeutics.

Meanwhile, scientists from Hong Kong and China have reviewed the broader landscape of milk exosomes in breast cancer treatment. Beyond acting as delivery vehicles for drugs like doxorubicin, paclitaxel, and 5-fluorouracil, milk exosomes may also have direct anti-tumor effects. They can promote apoptosis, interrupt the cell cycle, and regulate pathways such as NF-κB and STAT3. Combined with plant-derived compounds like curcumin and resveratrol, they form hybrid nanoparticles with enhanced therapeutic power.

For bioprocessing, the message is clear: milk exosomes are no longer a niche curiosity. They represent a scalable, safe, and highly adaptable platform for next-generation therapeutics—one that begins with biology’s oldest delivery system and may define medicine’s next one.

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Hopes Raised for More Sustainable Oligonucleotide Manufacturing

Large-scale manufacturing of oligonucleotides could become more environmentally friendly if the biotech industry can overcome the challenges of a promising technique for synthesizing them. That’s according to QurAlis, a clinical-stage biotech company targeting neurodegenerative disease.

Hagen Cramer, PhD, QurAlis’s CTO, thinks synthesizing oligonucleotides using enzymes could be more sustainable than traditional solid-phase synthesis methods, but challenges remain for the industry.

“Solid-phase synthesis is convenient—you can have everything automated, it’s fast, and can be used for [many] types of therapeutics,” he says. “However, because it’s a solid-phase synthesis, you have to wash away the external reagents with lots of solvents, and that’s why the mass intensity is high.”

By contrast, manufacturing RNA and DNA using a process that happens in nature and is aqueous-based uses fewer materials in the production of any given mass of product, notes Cramer. However, creating a wide selection of enzymes to manufacture multiple products remains a challenge for the industry.

“Enzymatic synthesis]was explored a long time ago, but it went away because people couldn’t figure out the challenges,” he points out. “But there’s now a lot more money in the industry as we have approved drugs and, hence, it’s now being reinvestigated.”

Other challenges include using enzymatic techniques for manufacturing above the 100-g scale and also speeding up these techniques to be comparable with solid-phase synthesis.

“With solid-phase synthesis, if you have a 20-mer oligonucleotide, you might have to take 80 chemical steps, and you can be efficient and complete all of that in a day, but—with an enzymatic approach—it’s going to take much longer and the development time is also large,” explains Cramer, adding that clinical-stage companies making smaller volumes may want to stick with solid-phase synthesis. But, he continues, commercial-stage companies producing large volumes of product may want to investigate enzymatic approaches as they become available.

“At a certain stage, if you’re working at commercial stage already, you can plan ahead and I think the industry will move toward these new approaches starting post-market,” he says.

The post Hopes Raised for More Sustainable Oligonucleotide Manufacturing appeared first on GEN – Genetic Engineering and Biotechnology News.

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Adaptive, Agent-Oriented Control for Biomanufacturing Systems

Agentic AI goes beyond predictive and generative AI and, in biomanufacturing, has the potential to enhance efficiency by integrating with existing manufacturing infrastructure such as IoT sensors, process information management systems, management execution systems, and even enterprise resource planning software. The challenge, however, is that industrial biomanufacturing processes are complex, demand resilience, and are tightly regulated.

The Adaptive Agent-Oriented System Control (AAOSC) framework developed by a team from the Technical University of Denmark (DTU) and SiC Systems addresses that challenge through a decentralized control layer. In it, “specialized autonomous agent ‘hives’ [are] coordinating digital twin enabled manufacturing infrastructure and real-time communications protocols.” The latter lets biomanufacturers integrate models, make learning-based inferences, and control process systems.

Four AAOSC case studies were discussed in a recent paper by Seyed Soheil Mansouri, PhD, professor at DTU and co-founder and CSO of SiC Systems and Christopher J. Savoie, PhD, co-founder and CEO of SiC Systems, and inventor of the agentic AI technology behind Siri. Those case studies “demonstrate AAOSO’s prowess [in] reducing deviating durations, averting shutdowns in severe fault scenarios, and boosting efficiency through virtual quantum and classical sensing and decentralized reasoning, all while aligning with regulatory imperatives…”

Despite its capabilities in monitoring process, identifying discrepancies, and recommending solutions, agentic AI “is not yet fully ready for complete, independent control in biopharmaceutical manufacturing,” Mansouri tells GEN. “Any AI that directly affects medicine quality still needs strong human oversight and full approval. We are getting closer, but full integration requires official [regulatory] clearance.”

The AAOSC framework that Mansouri and colleagues built may be unique in the industry. It isn’t all-knowing and “God-like,” he points out. Instead, “our methods are grounded in physics, chemistry, and biology within an agent ‘hive’—an orchestration of rule-based, mathematically informed agents. So, AAOSC is, foundationally, a different philosophy of building AI [in which] humans are in control.”

First, run in shadow mode

To introduce agentic AI, Mansouri advises starting gradually. “Run the AI alongside your current control systems in shadow mode—it watches everything and gives recommendations, but doesn’t make any actual changes without human oversight. This lets the teams learn how it works without any risks to production. Once confident, you can slowly expand its role while always keeping humans in final control.”

Both the FDA and EMA require systems that are fixed rather than continuously learning, he points out, and that can complicate adoption. To minimize the potential for regulatory issues that may arise by integrating AI into manufacturing processes, “work closely with your quality and regulatory teams from the beginning.

“Always maintain clear human responsibility, so no one is left wondering who is accountable if something goes wrong. Strong cybersecurity is essential,” Mansouri adds, “because these AI agents connect and talk to each other.” Therefore, “Start small, test thoroughly, and talk to regulators early.”

The post Adaptive, Agent-Oriented Control for Biomanufacturing Systems appeared first on GEN – Genetic Engineering and Biotechnology News.

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Loss of Smell Therapies Informed by Olfactory Receptor Spatial Mapping

A new study published in Cell titled, “A spatial code governs olfactory receptor choice and aligns sensory maps in the nose and brain,” led by researchers from Harvard Medical School (HMS) has created the first detailed map of the spatial distribution of over 1,000 olfactory receptors in the epithelium. The study informs the development of therapies for loss of smell, where treatment options are limited.

The researchers examined approximately 5.5 million neurons in more than 300 individual mice using single-cell sequencing and spatial transcriptomics. Results showed that neurons are organized into tight, overlapping, horizontal stripes from the top to the bottom of the nose based on the type of smell receptor expressed. This highly organized receptor map was consistent across mouse models and mirrored the organization of smell maps in the brain. Similar maps have been observed in vision, hearing, and touch.

Notably, the olfactory map was informed by a gradient of retinoic acid in the nose, which allowed each neuron to express the correct type of smell receptor based on its spatial location.  

“Our results bring order to a system that was previously thought to lack order, which changes conceptually how we think this works,” said Sandeep (Robert) Datta, PhD, professor of neurobiology at HMS and senior author and corresponding author of the study. “We show that development can achieve this feat of organizing a thousand different smell receptors into an incredibly precise map that’s consistent across animals.” 

The authors also found that the receptor map in the nose matches up with smell maps in the olfactory bulb of the brain, shedding insight into how information moves from the nose to the brain. 

While sensory maps that describe how receptors in the eye, ear, and skin are organized to capture and interpret auditory, visual, and touch information, mapping olfactory receptors has been a longstanding challenge due to high receptor diversity. As an example, mice have approximately 20 million olfactory neurons that express more than a thousand types of smell receptors, compared with only three main types of visual receptors for color vision. Each type of smell receptor detects a unique subset of odor molecules. 

The team is also studying smell receptors in human tissue to understand to what degree the smell map is consistent across species to inform treatments, such as stem cell therapies and loss of smell and its consequences, such as an increased risk of depression. 

“Smell has a really profound and pervasive effect on human health, so restoring it is not just for pleasure and safety but also for psychological well-being,” Datta said. “Without understanding this map, we’re doomed to fail in developing new treatments.” 

The post Loss of Smell Therapies Informed by Olfactory Receptor Spatial Mapping appeared first on GEN – Genetic Engineering and Biotechnology News.

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First Atlas of Female Reproductive System Maps Uncharted Menopause Biology

The first large-scale study across all major female reproductive organs has uncovered how aging processes impact each organ and tissue in unique ways. Published today in Nature Aging, the study has identified novel blood biomarkers that could help physicians anticipate health risks associated with menopause, such as pelvic floor prolapse. 

“Until now, we tended to consider menopause mainly as the end of the ovary’s reproductive function,” says Marta Melé, PhD, leader of the transcriptomics and functional genomics group at the Barcelona Supercomputing Center (BSC) and director of the study. “However, our results show that it acts as a turning point that profoundly reorganizes other organs and tissues of the reproductive system, and allow us to identify the genes and molecular processes that could be behind these changes.”

Menopause is a complex biological process with significant implications for overall health, which is estimated to be actively affecting 6% of the world’s population at any given time. However, the cellular and molecular processes driving it across reproductive organs and tissues have historically remained understudied. 

To map the complex biology of menopause, Melé’s team analyzed 1,112 tissue images and 659 RNA sequencing samples from 304 women between the ages of 20 and 70. This allowed the researchers to reconstruct aging trajectories of the uterus, ovary, vagina, cervix, breast, and Fallopian tubes. Using deep learning algorithms, they were able to identify key changes associated with aging in each organ, both at the molecular and tissue levels. 

Results showed that not all organs age uniformly across the female reproductive system. For instance, the ovary and vagina were shown to age gradually in a process starting years before menopause. Meanwhile, the uterus undergoes a very abrupt transition during menopause. 

Even within the same organ, different tissues were shown to age in distinct ways. In particular, the muscle tissue of the uterine wall and the vaginal epithelium were observed to be the most affected during menopause, undergoing sharp changes. 

The study also analyzed blood plasma samples from 21,441 women, which led to the identification of molecular signals of aging that can be detected in the blood. These biomarkers could offer non-invasive monitoring of female reproductive organs during menopause and enable more accessible, less invasive follow-up tests for women at risk of complications associated with menopause, such as pelvic floor prolapse. 

“We not only identified the molecular changes underlying the aging of these organs, but we also saw that they can be detected in blood, which opens the door to new clinical tools,” says Oleksandra Soldatkina, PhD, lead author of the study and researcher at BSC.

This study marks a step toward better understanding a key biological process that has historically been left behind, leading to better prevention, diagnosis and treatment of multiple diseases linked to menopause. The researchers highlighted that their findings “position menopause as a key inflection point in female aging and provide insights with tissue-specific focus to support healthier menopausal transitions and reduce age-related disease risk.”

The post First Atlas of Female Reproductive System Maps Uncharted Menopause Biology appeared first on Inside Precision Medicine.

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Infant formula largely safe from heavy metals, FDA finds

New contamination testing results from the Food and Drug Administration confirm the safety of infant formula in the U.S., the agency said Wednesday. 

The FDA tested 312 samples from 16 infant formula brands for contaminants like heavy metals, pesticides, and the “forever chemicals” known as per- and polyfluoroalkyl substances, or PFAS. The vast majority had undetectable or very low levels of contaminants, the agency said, with levels of lead, mercury, cadmium, and arsenic coming in below federal requirements for drinking water across all samples. 

Read the rest…

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Plasma Biomarker Patterns Distinguish Early-Onset Dementia

Plasma biomarker levels change in differing ways for different types of early-onset dementia, with unique clinical associations that could help stratify risk in patients, research suggests.

The findings may help improve detection and prognosis of these neurodegenerative diseases, which manifest before the age of 65 years and are often challenging to treat due to atypical symptoms and clinical heterogeneity.

The report, in JAMA Network Open, revealed differences in both the concentrations of biomarkers over time and their association with clinical outcomes in early-onset Alzheimer disease (EOAD) and frontotemporal dementia (FTD).

“Our results highlight disease-specific plasma biomarker dynamics and their potential utility in monitoring disease progression in early-onset dementia,” reported Eun-Joo Kim, PhD, from Pusan National University Hospital in Korea, and colleagues.

Recent developments with plasma biomarkers have changed the landscape of dementia diagnosis.

Phosphorylated tau 217 (p-tau217), a marker specific of Alzheimer’s disease, has been found to be highly accurate in detecting its pathology.

Meanwhile, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are emerging as astrocytic activation and neurodegeneration markers, respectively, with NfL particularly relevant for FTD.

Combining p-tau217 and NfL could therefore enable Alzheimer’s disease and FTD, two leading causes of dementia at an early age, to be distinguished.

To investigate further, Kim and team compared biomarker trajectories and clinical outcomes in 322 patients with EOAD and FTD, of whom 245 had EOAD and 77 FTD.

Around two thirds of each group was female, and the mean age was in the early to mid 60s.

High baseline levels of p-tau217, GFAP, and NfL were significantly associated with all clinical outcomes in the EOAD group, assessed using scores on the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating–Sum of Boxes (CDR-SB).

However, among patients with FTD, only baseline GFAP and NfL were associated with decreases in MMSE scores.

The association of p-tau217 and GFAP levels with clinical outcomes was greater at earlier stages of EOAD, with the former biomarker showing no association at later stages of disease.

The plasma biomarkers followed distinct longitudinal trajectories in the two forms of early-onset dementia. In the EOAD group, the levels of all three biomarkers increased significantly over time, but with FTD only NfL increased.

Annualized changes in levels of all three biomarkers showed outcome-specific associations with clinical decline in EOAD. GFAP and NfL changes were associated with declines in MMSE score and p-tau217 levels with worsening CDR-SB score in this group. No such associations were observed for patients with FTD.

“In this multicenter, prospective cohort study of patients with EOAD and FTD, the clinical relevance of plasma biomarker levels and longitudinal changes may vary between EOAD and FTD,” the authors summarized.

“These findings may inform future clinical practice and trial design regarding stratifying patient populations and monitoring clinical progression, particularly in EOAD.”

The post Plasma Biomarker Patterns Distinguish Early-Onset Dementia appeared first on Inside Precision Medicine.

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Single-Cell Survival Modeling Tool Offers New Precision in Cancer Prognosis

Oregon Health & Science University (OHSU) researchers have developed a first of its kind tool, scSurvival, that directly links information from individual tumor cells to patient survival outcomes, allowing clinicians to understand which specific cells are driving disease progression rather than treating them all the same.

“Traditional survival models in cancer rely on bulk data, which average signals across millions of cells and obscure important heterogeneity,” explained senior author Zheng Xia, PhD, associate professor of biomedical engineering in the OHSU School of Medicine and a member of the OHSU Knight Cancer Institute. “Tumors are highly complex ecosystems where different cell subpopulations can have very different and sometimes opposing effects on patient outcomes.”

He told Inside Precision Medicine that “scSurvival is designed to directly model survival using single-cell data, preserving this heterogeneity. Instead of treating a tumor as a single entity, it treats it as a collection of individual cells and learns which specific subpopulations are most associated with survival outcomes. This enables both more accurate prediction and deeper biological insight.”

The tool was designed using a statistical method known as an attention-based multiple-instance Cox regression framework, which constructs survival prediction models from single-cell cancer cohort data while simultaneously identifying cell subpopulations that are strongly associated with patient risk.

“The attention mechanism preserves cellular heterogeneity within each patient, allowing [cell] subpopulations with higher attention scores to be more closely linked to survival probability,” the researchers explain in Cancer Discovery. “The resulting outputs of scSurvival are the attention-adjusted hazard score for each cell along with patient-level risk scores.”

Xia and team tested the performance of scSurvival in two cohorts that included 32 patients with melanoma and 124 patients with liver cancer. Together, the cohorts provided single cell RNA sequencing data for more than 1.1 million individual cells.

They found that key immune cell types were enriched for higher- or lower-hazard cells. For example, monocytes/macrophages were enriched for high-risk subpopulations in both the melanoma and liver cancer cohort, but B cells were enriched for low-risk subpopulations in the melanoma cohort and high-risk subpopulations in the liver cancer cohort.

In both groups, the tool accurately predicted patient outcomes, with cells taken from melanoma patients who did not respond to immunotherapy having significantly higher hazard scores than those taken from responders.

Xia noted that the information scSurvival provides has several translational applications. “Differential gene expression between high- and low-risk cells can be used to develop prognostic biomarkers,” he said. “Pathways enriched in high-risk populations may reveal actionable therapeutic targets, while the abundance of specific cell types can support patient stratification for treatment selection. Importantly, these insights are derived at single-cell resolution, providing greater biological precision than bulk approaches.”

At present, scSurvival is primarily a research tool but Xia believes that longer term, it has potential clinical relevance. “For example, signatures derived from survival-associated cell populations could be translated into more practical assays (e.g., bulk RNA or targeted panels) for patient stratification,” he suggested. “However, direct clinical deployment would require further validation, simplification, and standardization.”

According to Xia, one of the biggest challenges to widespread adoption of the tool is the limited availability of large, well-annotated single-cell datasets with matched survival data, as single-cell sequencing is not yet routine in clinical workflows. But as more clinical trials adopt single-cell sequencing, he expects scSurvival to see broader use in resolving disease at cellular resolution.

The investigators now plan to extend the framework to incorporate spatial transcriptomics, which will allow them to account for how cells are organized within the tumor microenvironment. “We also aim to improve the model’s robustness across datasets and sequencing platforms, and to enhance its biological interpretability. Ultimately, we hope to translate the survival-associated signatures identified by scSurvival into clinically practical tests,” Xia said.

The study findings were also presented at the American Association for Cancer Research Annual meeting 2026 and the open-source scSurvival program and its tutorials are freely available at GitHub, Zenodo and Code Ocean.

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