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Macrocyclic Peptide Drugs Unlocked, Membrane Permeability Screened at Scale

Macrocyclic peptides are a promising drug modality that combine the oral convenience of small molecules with the high specificity of large biologics. Yet, they struggle with cell membrane permeability, limiting their ability to target disease interactions within cells.  

In a new study published in Nature Chemical Biology titled, “Generation of membrane-permeable cyclic peptides inhibiting protein–protein interaction”, researchers from École Polytechnique Fédérale de Lausanne (EPFL) have developed a new method to generate and screen large libraries of synthetic cyclic peptides to identify compounds that can enter cells for therapeutic effect. 

“We focused on small, less than 1000-Dalton, non-polar cyclic peptides that can enter cells by rapidly crossing the hydrophobic inner region of cell membranes,” says Christian Heinis, PhD, associate professor at EPFL. “The challenge was then to develop cyclic peptides with suitable shapes so that they can bind to targets of interest.” 

The authors focused on protein interactions linked to inflammation, oxidative stress, and neurodegeneration, and cancer. The study synthesized and screened a library of 15,360 fully random cyclic peptides, all designed to be small, compact, and relatively nonpolar to support membrane permeability. The screen identified several compounds capable of disrupting the disease-associated Keap1–Nrf2 interaction. 

The team optimized a cyclic peptide candidate, termed peptide 30, which combined strong target binding with membrane permeability. Peptide 30 inhibited the Keap1–Nrf2 interaction inside living cells in a dose-dependent assay. Compared with the natural Nrf2 sequence, peptide 30 had no electrical charge, fewer hydrogen bond donors, and lower polar surface area to support membrane permeability.  

The study demonstrated that membrane-permeable cyclic peptides can be developed without starting from known ligands, natural products, or binding motifs, broadening access to intracellular targets previously considered difficult to drug. 

“Our lab is now further advancing the technology to synthesize and screen even larger libraries of small, membrane-permeable cyclic peptides,” says Heinis. “And we are applying the technology to some of the most challenging protein–protein interaction targets, including big cancer targets like KRAS, b-catenin and c-Myc.” 

Heinis’s group has patented the method and founded the spin-off company Orbis Medicines, which recently raised more than €90 million in Series A funding to further develop and apply the technology for drug discovery. 

The post Macrocyclic Peptide Drugs Unlocked, Membrane Permeability Screened at Scale appeared first on GEN – Genetic Engineering and Biotechnology News.

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Multiomics Mass Spec Workflows in Drug Discovery

Advances in end-to-end multiomics platforms and the underlying scientific knowledge now enable faster and more precise biomarker discovery, mechanistic insight generation, and therapeutic design—core drivers of modern drug discovery programs. Within this integrated ecosystem, mass spectrometry-based metabolomics serves as a central analytical modality, offering the ability to quantify large numbers of metabolites from a single sample with high sensitivity and rapid turnaround.

Metabolomics supports biochemical pathway-level interpretation, where a primary biomarker can be contextualized alongside upstream and downstream metabolites to inform target identification, pathway modulation, and pharmacodynamic response assessment. Rather than focusing solely on the discovery of novel metabolites, emerging approaches emphasize the identification of characteristic metabolic signatures that differentiate disease states, therapeutic responses, or mechanistic subtypes.

Realizing this potential requires the development and deployment of AI enabled data analysis workflows that can reduce interpretation time, expand the breadth of detectable targets, and uncover complex patterns of metabolite perturbation. These capabilities ultimately enhance the precision and effectiveness of targeted therapeutic development.

 

Taraka Donti, PhD, is director of lab services at Revvity Omics.

The post Multiomics Mass Spec Workflows in Drug Discovery appeared first on GEN – Genetic Engineering and Biotechnology News.

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Personalized Cancer Vaccine Reduces Melanoma Recurrence by 49%

Results from the Phase IIb KEYNOTE-942 trial reveal that adding a personalized mRNA vaccine to standard immunotherapy after surgery can reduce the five-year risk of recurrence and death by 49% among patients with advanced melanoma. These findings were presented today at the annual meeting of the American Society of Clinical Oncology and published in the Journal of Clinical Oncology.

“Our study offers strong evidence to melanoma patients that intismeran vaccine therapy, when used in combination with immunotherapy, can demonstrably reduce their risk of  having their cancer return and improve clinical outcomes,” said Janice Mehnert, MD, professor in the department of medicine at NYU Grossman School of Medicine and associate director of clinical research at Perlmutter Cancer Center.

Melanoma is one of the most common forms of cancer. Although immunotherapies like pembrolizumab (Keytruda) have significantly improved outcomes for melanoma patients, this cancer is still known for its ability to evade the immune system and become resistant to treatment. 

Developed by Moderna, intismeran autogene is an mRNA cancer vaccine made specifically for each patient. The bespoke therapy is created by screening tumor samples for 34 neoantigens that can be leveraged to strengthen the immune system’s response against tumor cells and enhance the efficacy of immunotherapy. 

The KEYNOTE-942 trial recruited 157 patients with advanced stages of melanoma and at high risk of recurrence who were randomized to either receive standard pembrolizumab immunotherapy, or a combination of pembrolizumab and intismeran. Since the treatment was administered after surgery, intismeran was individually manufactured for each patient based on an analysis of their resected tumor. 

After five years, 68.8% of patients treated with the combination therapy remained alive and cancer-free, compared to 49.1% for those who received standard treatment. The addition of personalized vaccines also reduced the risk of developing a distant metastasis by 59%. 

“Now with five years of follow-up data, today’s results highlight the potential of a prolonged benefit of the intismeran autogene and Keytruda combination in patients with resected high-risk melanoma,” said Kyle Holen, MD, senior vice president and head of development, oncology and therapeutics at Moderna. “We continue to invest in our platform in oncology because of encouraging outcomes like these, which illustrate mRNA’s potential in cancer care.” 

A Phase III clinical trial is already underway to confirm the efficacy of intismeran as a first line therapy for melanoma in combination with pembrolizumab. Additional studies are looking into the effects of the cancer vaccine in other types of cancer, including non-small cell lung cancer, bladder cancer, and renal cell carcinoma. 

“Our findings also serve as encouragement to cancer researchers globally that mRNA vaccines like intismeran could work well in combination with immunotherapy for other cancers whose high rates of mutations have proven difficult to target,” said Mehnert.

The post Personalized Cancer Vaccine Reduces Melanoma Recurrence by 49% appeared first on Inside Precision Medicine.

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The Download: China’s brain implant ambitions

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

China has approved the world’s first invasive brain-computer chip—here’s what’s next

Sitting in the courtyard of his house in China’s Henan province last October, Dong Hui decided to try holding a pen. Six years after a car accident left him paralyzed from the neck down, he slowly wrote his name, “Thank you,” and the date.

The breakthrough was made possible by a brain implant called NEO. In March, it became the world’s first invasive brain-computer interface approved for use beyond clinical trials. The approval is expected to accelerate China’s push to become a global leader in brain implants.

Read the full story on how China reached this milestone—and what it means for the future of brain-computer interfaces.

—You Xiaoying

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Nvidia is launching its first AI chip for personal computers
The RTX Spark will power laptops from Dell, HP, Microsoft, and others. (BBC)
+ They’re being designed specifically to run AI agents. (WSJ $)
+ The first devices are set to launch on Windows PCs in the fall. (CNBC)
+ The move marks a challenge to Apple and Intel. (FT $)

2 The US is stopping exports of AI chips to Chinese firms abroad
It’s closed a loophole allowing exports to Chinese subsidiaries. (Reuters $)
+ Which may have enabled unlicensed access to Nvidia chips. (Al Jazeera)
+ Export curbs have led China to redesign its chip industry. (MIT Technology Review)

3 Surgeons have transplanted pig liver and kidneys into a living person
The clinically dead recipient’s organs worked for almost five days. (Nature)
+ Pig organs could ease transplant shortages. (Guardian)
+ Putin says organ transplants could grant immortality. (MIT Technology Review) 

4 The US, Australia, and UK will defend seabed cables with underwater drones
They’re developing the vehicles via the trilateral AUKUS defense ⁠pact. (CNN)
+ Undersea internet cables face growing threats. (BBC)

5 A new study has revealed chatbots’ manipulative ‘dark patterns’ 
It found they prey on emotions to encourage harmful behavior. (404 Media)
+ They can also sway voters better than political ads. (MIT Technology Review)

6 Apple plans to disrupt the traditional glasses market
Its smart glasses target the broader spectacles industry. (Bloomberg $)
+ Smart glasses are also gaining traction in warfare. (MIT Technology Review)

7 AI super PACs are dueling over the midterms
Split between Anthropic and OpenAI, they’re fighting to shape AI regulation. (NYT $)

8 SoftBank has overtaken Toyota as Japan’s most valuable company
The AI boom pushed SoftBank’s market value above $305 billion. (Bloomberg $) 

9 A botnet of more than 17 million devices has been dismantled in Europe
Dutch authorities linked the network to a Russian proxy service. (Ars Technica)

10 Tech leaders are uniting around a transhuman vision for AI
They’re working toward a post-human agenda. (Guardian)

Quote of the day

“It’s just been shoved down their throats in secrecy. And that makes them upset.” 

—Legendary environmental activist Erin Brockovich tells “The Jim Acosta Show” why citizens are angry about data centers expanding into their communities.

 One More Thing

Dr. Nicholas Passalacqua, Forensic Anthropology Facilities Director at Western Carolina University observes a body at the decomp facility.

MIKE BELLEME


What happens when you donate your body to science

Rebecca George doesn’t mind the vultures. At Western Carolina University’s body farm, forensic anthropologists monitor donors—sometimes for years—as they become nothing but bones.

Around 20,000 people donate their cadavers to scientific research and education each year. At anatomy labs and body farms, they help train doctors, advance research, and teach scientists more about the human body long after death.

But what actually happens after a body is donated? Read the full story to find out.

—A.W. Ohlheiser

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ This map of moments turns the planet into a shared diary.
+ Let editors curate your ideal podcast moments with this app.
+ Architecture lovers will enjoy this encyclopedia of famous buildings.
+ Get in touch with your emotions through this map exploring more than 100 feelings.

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Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI

IntroductionThree-dimensional assessment of fetal cortical morphology from MRI is essential for understanding early brain neurodevelopment. However, measurement can be affected by fetal imaging quality, number and selection of available stacks, and reconstruction methods.MethodsWe evaluated the within-session reliability of an automated cortical morphometry pipeline in 30 typically developing fetuses [22–36 weeks gestational age (GA)]. For each subject, two disjoint subsets of 2D T2-weighted stacks (no shared stacks) were independently reconstructed into 3D volumes using the Neural Slice-to-Volume Reconstruction (NeSVoR) and the Slice-to-Volume Reconstruction Toolkit (SVRTK). Cortical plate volume, surface area, mean sulcal depth, and absolute mean curvature were extracted, and measurement reliability was assessed using absolute percent difference (APD) and intraclass correlation coefficients (ICC). Multiple linear regression evaluated the effects of mean stack quality, quality difference between subsets, stack count, and GA on measurement reliability.ResultsNeSVoR-derived metrics showed high reliability for all measures (mean APD < 3%, ICC > 0.99). SVRTK-derived metrics were also robust (mean APD < 5%, ICC > 0.97). Reliability increased with greater stack count and older GA in NeSVoR, and with higher mean stack quality in SVRTK.DiscussionThese results demonstrate that automated cortical morphometry from fetal MRI yields highly consistent measurements of volumetric and surface metrics within the proposed within-session design, once minimum levels of image quality and stack count are met. This study proposes a within-session benchmark for automated fetal cortical measurements and underscores that systematic reliability assessment is essential for confident application of automated pipelines in fetal neuroimaging.
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Domain-aware domain–class adaptation network for motor execution to motor imagery EEG classification

IntroductionMotor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.MethodsTo tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain–class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.ResultsExtensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.DiscussionTo the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.
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GLOBE: an explainable machine learning platform for preoperative prediction of thromboembolism and neurological deterioration in patients with glioma

BackgroundPatients with glioma are at high risk of postoperative venous thromboembolism (VTE) and postoperative neurological deterioration (PND). Conventional clinical scoring systems have limited accuracy in predicting these perioperative risks. This study aimed to develop and validate machine-learning models for individualized preoperative prediction of postoperative VTE and PND in patients with glioma.MethodsA retrospective cohort of 427 patients with glioma was included. Patients were randomly divided into training and test sets at an 8:2 ratio using stratified random sampling. Multiple machine-learning algorithms were trained and evaluated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. An online prediction platform was developed to facilitate individualized risk assessment.ResultsAmong 427 patients, postoperative VTE and PND occurred in 34 and 35%, respectively. For VTE prediction, the final Top-10 random forest model outperformed the Caprini score alone and achieved an AUC of 0.815 (95% CI, 0.720–0.910) in the held-out test set. Performance remained strong in the clinically significant VTE sensitivity analysis (AUC, 0.923; 95% CI, 0.847–0.998). SHAP analysis indicated that older age, elevated D-dimer and fibrin degradation products (FDP), as well as lower hemoglobin levels, were associated with increased predicted VTE risk. For PND prediction, the final Top-10 logistic regression model achieved an AUC of 0.741 (95% CI, 0.627–0.854). Older age, recurrent glioma, higher Caprini score, higher neutrophil percentage, and hypertension history tended to increase predicted PND risk. Models were deployed in the GLOBE web platform (https://gliomas.shinyapps.io/GLOBE/) for real-time preoperative risk prediction.ConclusionWe developed accurate, interpretable, and clinically meaningful preoperative prediction models for postoperative VTE and PND in patients with glioma. The GLOBE online prediction system translates these models into a practical tool for individualized perioperative risk stratification.
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Efficacy of repetitive transcranial magnetic stimulation for insomnia disorder: a systematic review and meta-analysis of randomized controlled trials

ObjectiveInsomnia Disorder (ID) is associated with significant health burdens. First-line treatments are limited by accessibility or side effects, necessitating alternative approaches. rTMS, a noninvasive neuromodulation technique, has shown promise in treating various neuropsychiatric disorders by modulating cortical excitability. This comprehensive meta-analysis explores the effect of rTMS on ID and identifies possible factors that influence it.MethodsA comprehensive search of the Cochrane Library, Embase, Web of Science, PubMed, CNKI, and Wanfang databases identified RCTs evaluating the effects of rTMS on insomnia disorder. Data synthesis and subgroup analysis were performed via SMD, WMD, relative risk (RR), and 95% CI to evaluate the effects of rTMS and its influencing factors. The review protocol was prospectively registered in PROSPERO (CRD42024626833).ResultsNineteen studies contributed 23 trials involving 1,690 adult participants. The rTMS group demonstrated markedly improved sleep quality compared with sham rTMS recipients in individuals with insomnia disorder. (PSQI total scores; ISI; p < 0.001); (PSG (SE); p = 0.003). Combined rTMS and medication were more effective than medication alone. (PSQI total scores; p = 0.003). In the subgroup analysis, after excluding a study with high heterogeneity, the rTMS cohort showed greater improvement in sleep quality than the other treatment groups. (PSQI total scores; p = 0.03).ConclusionIndependent rTMS and rTMS-medication combinations significantly improve sleep patterns and rest quality in patients with Insomnia Disorder. The safety and efficacy of LF-rTMS are also significant. The duration of the disease, treatment duration, and stimulation site may influence the sleep quality of patients with ID.