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Breath Sensor Monitors Fat Metabolism at Home

Scientists in Switzerland have developed a portable breath detector that can accurately measure acetone released into the breath when the body burns fat. The smartphone-assisted device allows patients to monitor their metabolism at home and could help doctors personalize treatment for metabolic diseases such as obesity and diabetes.

Acetone levels in the breath have long been recognized as an indicator of metabolic activity, since its concentration rises when the body shifts from using carbohydrates to fats as the primary energy source. However, accurate measurements have traditionally required bulky and expensive laboratory equipment, while consumer devices have lacked the sensitivity needed to reliably measure acetone, especially at lower concentrations. 

“If we want to make that information available to patients, we need to shrink those technologies into compact, user-friendly devices,” said Andreas Güntner, PhD, assistant professor at ETH Zurich and senior author of the study published today in the Device journal.

To make a compact breath analyzer, Güntner’s team used a chemoresistive sensor that changes its electrical properties in the presence of acetone. These types of sensors are known for their high sensitivity, rapid response, low power consumption, and small size. During each measurement, the accompanying smartphone app coaches users to exhale with the right force and duration, while quality controls can reject improper breaths or contaminated air. This design makes the sensor easy to use while ensuring accurate measurements. 

The breath detector was used to analyze 312 breath samples from 12 healthy adults, with measurements closely matching those obtained using gold-standard mass spectrometry. “These findings show that we have the high performance needed for applications such as clinical studies, where you really want to distinguish these slight differences in fat metabolism,” said Simone Hersberger, graduate student at ETH Zurich and first author of the study. 

The researchers then used the sensor to monitor breath acetone under four metabolic scenarios: light exercise followed by a high-carbohydrate meal, intense exercise followed by a high-carbohydrate meal, a high-fat ketogenic meal, and fasting. Breath acetone levels remained low during light exercise but increased with intense exercise, dropping after a high-carbohydrate meal. Acetone levels rose after a ketogenic meal and increased further during fasting. 

Through Alivion, a spin-off from ETH Zurich, the technology is already available to individuals interested in tracking breath acetone to monitor weight loss and athletic performance. The device is currently being used to monitor individual progress in clinical studies of epilepsy, where a ketogenic diet is a standard medical treatment. 

“Now it’s really time to spread it out into clinical trials and answer questions such as the effectiveness of different fasting therapies by providing personalized guidance,” said Güntner. “We’re really moving toward healthcare solutions that, in the future, you won’t need to go to the hospital for anymore. You’ll be able to do them at home.” 

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Malaria Drug Reveals Genetic Vulnerability Across Cancers

Scientists have found that the antimalarial drug quinacrine can exploit a vulnerability in cancers that become reliant on the NDRG1 protein to sustain their DNA damage response. Published in Science Signaling, their study not only identifies NDRG1 as a promising therapeutic target, but also demonstrates a new strategy for discovering similar vulnerabilities across multiple types of cancer.

“Our findings suggest that NDRG1 expression could serve as a biomarker to help identify patients most likely to benefit from therapies targeting this pathway,” said Garik V. Mkrtchyan, PhD, assistant professor at the University of Copenhagen and lead author of the study. “We further showed that high NDRG1 expression predicts poor survival across multiple cancers, and that inhibiting NDRG1 creates vulnerabilities, highlighting new opportunities for precision oncology.”

Mkrtchyan and colleagues set out to identify new targets for cancer therapeutics by leveraging the concept of synthetic lethality—a phenomenon where cancer cells can survive the loss of one of two genes but die when both are inhibited. This approach has already proven successful in ovarian, breast, and prostate cancers with BRCA mutations, which are particularly vulnerable to PARP inhibitor drugs. 

“In oncology, this concept is particularly promising because cancer cells often harbor mutations in specific DNA damage response pathways, making them highly dependent on the remaining repair mechanisms for survival,” said Mkrtchyan. “Targeting these dependencies enables selective elimination of cancer cells while sparing healthy tissue.”

Using transcriptomics data, the researchers identified quinacrine as a promising candidate for disrupting the DNA damage response by targeting the stress-response protein NDRG1. Quinacrine has been used as an antimalarial drug for nearly a century, later gaining approval as a treatment for lupus. In recent years, the compound has attracted growing interest as a potential cancer treatment. 

Screening through hundreds of cancer cell lines revealed that blood cancers, which generally showed high NDRG1 expression, were the most sensitive to the drug. Colorectal cancer cells were also sensitive to quinacrine, especially those with mutations in the MLH1 and PARP3 genes. The team later confirmed these findings in patient datasets, where high NDRG1 expression together with loss of either of these genes correlated with improved survival rates. 

“While quinacrine has previously been reported to possess anticancer activity, our study uncovers upstream mechanisms of its action on DNA damage response,” said Mkrtchyan. “By applying an automated robotics screen across more than 130 cancer cell lines, we identified novel synthetic lethal interactions involving NDRG1, providing a framework for discovering new therapeutic vulnerabilities across multiple cancer types.”

Despite its potential as a cancer therapy, quinacrine can potentially cause unwanted side effects. The researchers therefore plan to explore alternative drug candidates that can inhibit NDRG1 with more potency while reducing toxicity.

“The next steps will be to develop small molecules that inhibit NDRG1 with greater potency and specificity than quinacrine, thereby minimizing potential off-target effects,” said Mkrtchyan. “From a translational perspective, we aim to validate the identified synthetic lethal interactions in preclinical tumor models and investigate whether targeting the NDRG1 axis can overcome treatment resistance across a broader range of cancers.”

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ctDNA Blood Test Better Targets Radiopharmaceutical Prostate Cancer Therapy

A new ctDNA blood test from a Japanese team identifies those metastatic, castration-resistant prostate cancer (mCRPC) patients most likely to benefit from 223Ra radiopharmaceutical therapy. The test also monitors their progress throughout treatment.

The study appeared in the July issue of The Journal of Nuclear Medicine. The lead author is Masaki Shiota, MD, PhD, associate professor in the Department of Urology in the Graduate School of Medical Sciences at Kyushu University in Fukuoka, Japan. 

Prostate cancer is very biologically heterogeneous. Patients who appear clinically similar may have different responses. Better tests are needed to match patients to therapies and determine the optimal treatment sequence or combination of therapies.

223Ra dichloride targets bone metastases and can improve overall survival and quality of life in CRPC patients. It is an alpha-particle–emitting radionuclide that behaves chemically like calcium. It is therefore preferentially incorporated into areas of increased bone turnover, including the osteoblastic lesions commonly produced by prostate cancer bone metastases.

But the treatment is expensive, clinical outcomes vary among patients, and there is not yet a reliable biomarker to predict or monitor treatment response.

This study used a research-based 88-gene panel with deep sequencing of plasma circulating tumor DNA (ctDNA) and matched leukocyte DNA. “The leukocyte analysis was important for distinguishing true tumor-derived alterations from germline variants and age-related clonal hematopoiesis,” Shiota told Inside Precision Medicine.  

Prostate cancer is one of the most commonly diagnosed malignancies in men. Metastatic castration-sensitive prostate cancer arises either de novo as a cancer, or as a recurrence after radical local therapy for localized disease. It accounts for approximately 10% of newly diagnosed prostate cancer cases in Japan.

This team tracked associations between ctDNA profiles and clinical outcomes, including biomarker response, radiographic progression-free survival, and overall survival. 

Patients with a higher amount of tumor DNA in the blood or certain gene changes, such as TP53, PTEN, and cell cycle pathway alterations, detected through ctDNA testing before treatment, had worse outcomes. Further, changes in tumor DNA reflected treatment response and disease trajectory.

“While 223Ra is an important treatment for prostate cancer that has spread to the bones, not all patients benefit equally,” said Shiota. “Our findings suggest that a blood-based genomic test may help identify patients who are more likely or less likely to benefit from the therapy. This could help doctors choose treatment more carefully and monitor patients more closely, with the goal of providing more personalized care.”

One of the biggest hurdles to the test’s advancement is reimbursement.

This depends on the country, the clinical indication, and the specific assay used. In Japan, Shiota explained, certain approved comprehensive genomic profiling tests using blood, such as FoundationOne Liquid CDx, can be covered by the national health insurance system under defined conditions. These tests are primarily used to identify potentially actionable alterations in patients with advanced solid tumors, particularly when obtaining adequate tumor tissue is difficult.

“The research assay used in our study is not currently reimbursed for selecting patients for radium-223 or for monitoring their response during treatment,” said Shiota. “Before insurance coverage could be considered for this particular use, our findings would need to be externally validated, and prospective studies would need to demonstrate that ctDNA-guided treatment decisions improve patient outcomes.”

ctDNA has several important applications in precision oncology. For example, it can: identify actionable genomic alterations and thereby help select targeted therapies, be used to monitor treatment response and detect emerging resistance mechanisms, capture tumor heterogeneity more comprehensively than a biopsy from a single metastatic site, detect minimal residual disease after treatment, and identify molecular relapse before it becomes apparent on conventional imaging.

The post ctDNA Blood Test Better Targets Radiopharmaceutical Prostate Cancer Therapy appeared first on Inside Precision Medicine.

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Transcripta Bio Raises $24M for AI-Driven Neurological Disease Therapies

Chris Moxham, PhD, has been a “drug hunter” for three decades, building data-driven platforms as vice president of quantitative biology at Eli Lilly, and CSO of Folcrum Therapeutics, in recent years. 

“Technology is now advancing to allow us to interrogate the transcriptome, which is a phenomenal blueprint for cell state and fate,” he told GEN Edge.  

Moxham currently leads Transcripta Bio as founder and CEO. The AI-driven drug discovery start-up is developing small molecule therapeutics to modulate gene expression disease signatures. 

Transcripta has now announced a $24 million funding raise to advance IND-enabling studies and clinical preparation of its neurological disease portfolio, including autism spectrum disorder (ASD) and facioscapulohumeral muscular dystrophy (FSHD). Mayo Clinic and Omnimed will join JAZZ Venture Partners, BlueYard Capital, and a group of life sciences family offices, as investors. 

Phase II clinical trials are “where the rubber meets the road,” says Moxham. He emphasizes that de-risking therapies early is key to improving drug discovery success rates, which often fall below 10%. 

Founded in 2023, the Palo Alto-based company currently houses fifteen employees. “This isn’t a company you could have built five years ago,” highlighted Moxham. He cites the intersection of scalable sequencing technology, compute power, and lab automation among the factors enabling the rise of AI-driven biology. 

Three-pronged approach 

While much of the field has defined transcriptome AI models as the “virtual cell,” Moxham emphasizes Transcripta’s translational focus. 

The company’s proprietary platform takes a three-pronged approach. First, disease signatures are identified using patient-derived single cell RNA-seq (scRNA-seq) data. An in-house generated “drug atlas” then measures the effects of small molecule perturbations across 80% of the transcriptome, capturing full dose-response profiles in diverse cellular contexts, including glutamatergic and motor neurons, fibroblasts, and keratinocytes. These data power AI models that identify promising compounds that can therapeutically modulate gene expression.

Transcripta’s neurological disease pipeline is structured as a tiered portfolio of novel molecules and repurposed clinical-stage assets, with the latter benefiting from existing human safety data that can shorten development timelines and lower costs. 

In 19q12 syndrome, a form of ASD, the team demonstrated that entrectinib, an FDA-approved oncology drug, could reverse disease when given at low concentrations. One patient case demonstrated clinical benefit within nine months after taking the drug.  

In Huntington’s disease, Transcripta’s platform identified novel molecules that could downregulate DNA mismatch repair protein and validated therapeutic target, MSH3. The company plans to file an IND next year. Transcripta is also pursuing pre-IND research in FSHD and myonic dystrophy.  

Moxham emphasizes the generalizability of the platform. “We are now looking at hundreds of diseases with this type of approach,” he says.  

Transcripta plans to introduce another cohort of therapeutic programs by Q2 of 2027.

The post Transcripta Bio Raises $24M for AI-Driven Neurological Disease Therapies appeared first on GEN – Genetic Engineering and Biotechnology News.

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AI Protein Engineering Model Designed for Biomanufacturing

A cell engineering model designed specifically for the biomanufacturing environment could go a long way toward optimizing host cell performance, reducing development timelines, and streamlining biomanufacturing platform design.

“There’s a real challenge in determining how to engineer a host cell’s genome to maximize its performance in a biomanufacturing application.” That’s the difficulty laid out by Shawn Manchester, PhD, CEO, Triplebar Bio, and a view that’s shared by Yun Song, PhD, professor, University of California, Berkeley, and the industry innovation organization BioMADE. The three, with support from the National Science Foundation, are developing such an AI-informed cell engineering model to solve that challenge.

What’s most notable is that this AI-informed predictive and optimization model focuses on the biomanufacturing environment. The large-scale, application-specific training database is being designed to handle bench- to commercial-scale biomanufacturing.

Biomanufacturing-relevant environment

As Manchester says, “There aren’t a lot of organisms that have naturally evolved to make proteins they’ve never seen before, in an environment they’re not well-evolved for. Our ability to generate data in a setting that is relevant to biomanufacturing and to do so at the scale of hundreds of thousands of cells and data points is innovative…and it’s not something that many others are looking at. Most people are working on AI for protein engineering of therapeutics and other molecules as opposed to cell engineering for use in biomanufacturing.”

This project focuses on data capture from Pichia pastoris as the host cell producing five different proteins that are relevant for bioprocessing, food production, and defense. The learnings will be incorporated into Triplebar’s other programs, too, including optimization for its Chinese hamster ovary (CHO) cells, Manchester says.

“Improving the scalability of these proteins directly benefits [not just bioindustrial or biopharmaceutical applications, but a broad range of] biomanufacturing,” Brandon Simmons-Rawls, program manager, BioMADE, emphasizes.

This AI project combines two core technologies deployed at Triplebar:

  • Droplet microfluidics, in which a cell is encapsulated in a water and oil emulsion alongside a fluorescent-based sensor to quantify protein production
  • Multimodal transformer-based AI model

The genomes and transcriptomes of cells that make either more or less protein than baseline are sequenced and fed into the AI’s very large, labeled training sets to identify correlations between genotypes and phenotypes. These correlations are important patterns about how the genome works, Manchester explains, and can be used to generate new genomic designs for optimization of the host cell to the biomanufacturing process.

“We first pre-train models on all relevant sequencing data from thousands of genomes, which gives us an underlying structure to the genome. Then we fine-tune those models with the genotype-phenotype data we generate in the biomanufacturing-relevant environment. This allows us to understand which of those patterns in the genome relate to biomanufacturing-specific performance,” Manchester says.

Once this AI-enabled model rolls out, Manchester says he envisions it being used by bioindustrial and biopharma companies that “need to improve the productivity or efficiency of the organism they use in manufacturing. Results should be faster and more efficient than those derived using guess-and-check methods based on our current understanding of how these organisms work.

“The goal is for people to log onto this AI tool, identify what they’re making and what they’ve done, and ask the AI what else they can do to improve performance,” he continues. “The model will serve them specific genetic designs that they can deploy in their organism.”

Manchester predicts that more than 10% of the designs will produce meaningful improvements—significantly more than traditional, early-stage design-build-test cycles. This streamlines the cell engineering cycle by focusing on modifications likely to yield performance improvements that are most meaningful for biomanufacturing.

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Cell-Therapy Manufacturers Get Creative on Checking Particulates

Manufacturers of cell-containing products are adopting new techniques to work around unclear regulatory guidance on particulates. That’s the experience of Diana Colleluori, PhD, principal chemistry, manufacturing and controls (CMC) consultant at Biologics Consulting.

According to Colleluori, cell-containing products can be harder to visually inspect for particulates, as they’re not clear and are often stored in opaque bags.

“Processing and testing cell-containing products already has challenges because they can’t be terminally sterilized,” she explains. “It’s also harder to make a visual assessment to meet regulatory requirements because they already contain cells and the final product is usually in a cell-freezing bag.”

The particulates are mostly (90%) plastic that enter the product from contact with the inside of single-use equipment used during manufacturing, she says, adding that other particulates can enter from product manipulation and, thus, it’s best to try to minimize this.

Companies that Colleluori has worked with have tackled this problem by running their process with the formulation buffer, but minus cells, she continues. This allows them to assess particulates in a clear solution, outside of a bag, which they can then extrapolate to running the same process with cells.

“By testing visible and sub-visible particles in those samples, you can prove your product contact materials are expected to meet the limits for your cell-containing products,” according to Colleluori.

The company can also run quality control tests on a small sample of the final product. Among the tests carried out are those based on the United States Pharmacopeia (USP) chapters 790 for visible particulates and injections,1790 for visual inspections of injections, and 788 for the sub-visible particles.

In addition, EU guidance on visual inspection of particles can also be relied upon. Which tests are run should depend on the method of administration, adds Colleluori.

“If you’re doing an intramuscular injection, it’s probably a little less rigorous than an intrathecal injection of products that have stricter regulations on the number of particles and sub-particles that can be present,” she says.

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Scaling Cell and Gene Therapy Manufacturing

As cell and gene therapies (CGTs) move closer to mainstream clinical use, manufacturers are under growing pressure to bridge the gap between scientific breakthroughs and commercial reality. To explore this transition, GEN talked with three industry leaders, and here’s what we learned.

GEN: In today’s bioprocessing for cell and gene therapies, what is the most crucial business challenge and why?

Jonathan Wofford, COO, Title21 Health Solutions: Currently, the most crucial business challenge to solve in cell and gene therapy bioprocessing is scalable, cost-effective manufacturing. While scientific advances and clinical outcomes have accelerated the demand for therapy development, producing consistent, high-quality products at commercial scale remains difficult due to the increasing complexity of processes, limited automation, underdeveloped data-management infrastructures, supply-chain constraints, and patient-specific workflows for autologous therapies.

Justin Irizarry, CEO, OrganaBio: The disconnect between starting material and everything downstream. Despite heavy investment in manufacturing and analytics, the quality and consistency of raw biological material still determines whether a process succeeds. Variable, fragmented sourcing becomes failed runs, slipped milestones, and regulatory risk, and you can’t engineer that variability back out of a living product.

Carol Houts, CEO, Germfree: The most pressing challenge in CGT bioprocessing today is the gap between clinical promise and manufacturing readiness. Developers are advancing therapies faster than the infrastructure, workforce, and supply chain can scale to support them, and that mismatch is costing time and patients.

GEN: What is the most effective way to address these challenges?

Wofford: In theory, the approach should focus on end-to-end process standardization and automation, supported by closed-system manufacturing, digital process infrastructure, and scalable platform technologies. This reduces variability, decreases labor costs, improves regulatory compliance, increases throughput, and enables consistent product quality from clinical development through commercial production. I say in theory because adoption may be limited by the significant upfront investment needed to implement advanced manufacturing technologies.

Irizarry: Vertical integration and co-location: control the chain from donor through processing and testing under one quality system, with a recallable donor pool, identical SOPs across RUO and GMP, and processing near collection sites.

Houts: The most effective response is committing to manufacturing strategy early, not as an afterthought to clinical development. That means purpose-built environments, modular infrastructure that can grow with the program, and partners who understand GMP from day one. The groundwork determines whether a therapy ever reaches the people who need it.

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Hybrid Single-Use and Stainless-Steel Plants the Greener Option

Hybrid biopharmaceutical manufacturing facilities that combine stainless steel (SSTs) and single-use (SUTs) technologies are the “sweet spot” from an environmental sustainability perspective, according to new research. The research compared the environmental impact—specifically, the carbon footprint— of hybrid facilities with those where SUTs are used, arguing that ongoing efforts to decarbonize energy generation may have shifted the metrics.

And the key findings are that the CO2 footprint of SUTs is significantly higher than previously assumed and that key SST process steps can now have a lower carbon footprint than their single-use counterparts.

Lead author Jan Reiners, rer. nat. , from Roche, tells GEN, “For a long time, SUT was considered more sustainable because it avoids the energy needed to clean and steam-sterilize stainless steel. However, this paradigm has now inverted due to the rapid decarbonization worldwide due to the expansion of renewable energy.

“A single-use run at the 2,000L scale generates up to 6.5 tons of CO2e per batch only in plastic and packaging waste. We didn’t do this assessment for a full stainless-steel system, but the hybrid facility used less than half of plastics,” he adds.

Reiners and colleagues used “market-based emissions accounting” data for the analysis, noting it is becoming common for drug companies to decouple emissions from local energy supplies through tools like power purchase agreements and on-site photovoltaic (PV) installations.

“For instance, our own organization, Roche, has recently achieved its global goal of sourcing 100% sustainable electricity across all worldwide operations. And we are not alone: AstraZeneca is reducing energy emissions by 98% by 2026 and transitioning to 100% renewable energy for heat and power. Novartis claims to have become carbon neutral for energy in 2025, as do many other companies,” he says.

The researchers also found that some long-standing assumptions about SUT systems— for example, that they have lower HVAC costs—no longer hold.

“In modern biomanufacturing, cleanroom ISO classifications remain identical for both SUT and SST due to functionally closed processing, while the massive logistics and warehousing required to store SUT consumables actually expand physical space requirements,” continues Reiners. “And finally, the mass of fossil-fuel-based plastics was underestimated and its CO2 impact actually increased by ~30% in updated global life cycle databases like Ecoinvent v3.12.”

Hybrid alternatives

Considering the findings, Reiners and co-authors suggest that, to minimize emissions and costs, biopharmaceutical manufacturers need to combine stainless-steel systems with SUTs.

“Although SUT is less sustainable, it has other advantages like faster product changeover, more flexibility, less investment, but higher running costs. A hybrid facility is the ‘sweet spot’ of combining SUT and stainless steel to reduce the CO2 impact,” he maintains. “Depending on the facility layout, various measures can reduce the footprint, but per se it is about retaining stainless steel for simple, high-volume, resource-heavy operations—like buffer and media preparation—while keeping single-use for the complex, flexible core process like bioreactors.

“In our study, comparing a full-SUT facility to a hybrid facility reduced the material-related carbon footprint by 62%, dropping emissions from 6.5 tons of CO2e to just 2.8 tons of CO2e per batch.”

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Congo’s Ebola outbreak has killed over 1,000 people

BUNIA, Congo — More than 1,000 people have died in the Ebola outbreak in Congo, according to Africa’s top health body, a grim toll in the fastest Ebola outbreak in history, as conflict, community resistance and an uneven response fuel its spread.

Speaking at a health summit in Ghana on Wednesday, Dr. Jean Kaseya, director-general of the Africa Centres for Disease Control and Prevention, said 1,031 deaths have been confirmed.

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