Tag: Academic research

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Nextgen Platform Combines VectorBuilder and Maxcyte Technologies to Boost Clinical-Grade Cell Engineering

VectorBuilder and MaxCyte formed a strategic partnership focused on co-developing a new gene delivery solution using VectorBuilder’s MiniVec™ plasmid system and MaxCyte’s clinical electroporation platform for ex vivo cell engineering.

Officials at both companies say that ex vivo cell therapies such as CAR-T, CAR-NK, and iPSC-based treatments have gained significant traction, but critical challenges in safety and manufacturability continue to limit their broader application. They explain that existing gene delivery methods present significant trade-offs: traditional electroporation of conventional DNA or RNA often results in poor target cell viability or limited therapeutic durability, while lentiviral vectors, though widely used, carry high production costs and potential safety risks such as malignancy arising from vector integration into the host genome.

What is collectively needed, both companies maintain, are more efficient, safer, and scalable approaches to cell engineering.

The partnership between VectorBuilder and MaxCyte has been designed to address these challenges by developing the next-generation electroporation-based ex vivo gene delivery solution through the integration of VectorBuilder’s MiniVec backbone with MaxCyte’s Flow Electroporation® technology.

MiniVec is described as a miniaturized plasmid backbone that eliminates the need for antibiotic- or additive-based selection during fermentation to simplify translation to GMP-grade production. Its reduced prokaryotic sequences have been shown to improve yield and performance across a broad range of applications, according to VectorBuilder.

Flow Electroporation relies on a continuous-flow process that reduces cellular stress, preserving cell viability and functionality while enabling scalable, highly efficient gene delivery. This two-pronged approach is designed to deliver significantly improved cell viability and higher transfection efficiency compared to conventional models, pointed out Maher Masoud, president and CEO of MaxCyte.

“Cell therapy development requires delivering therapies that are manufacturable, scalable, and commercially viable,” he said. “We believe we can enable a new standard for nonviral gene delivery—one that enhances cell quality, improves manufacturing efficiency, and provides developers with a more streamlined path from research through commercialization.”

“This partnership combines our complementary strengths to establish a next-generation platform for efficient, safe, and scalable electroporation-based cell engineering for therapies such as CAR-T.

“As both companies have extensive expertise in GMP-compliant clinical development solutions, the combined platform is well aligned to enable a seamless development pipeline from clinical trials through to commercialization,” noted Bruce Lahn, PhD, founder and chief scientist of VectorBuilder. “Our aim is to provide a platform delivering high-end performance with an optimal cost-of-goods and price-per-dose model to ease scale-up, commercial strategy, and fundraising efforts for drug developers.”

The post Nextgen Platform Combines VectorBuilder and Maxcyte Technologies to Boost Clinical-Grade Cell Engineering appeared first on GEN – Genetic Engineering and Biotechnology News.

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Quantum Mechanics Principles Help Researchers Build Cancer Prediction Model

A team of researchers at the University of Utah has developed a quantum mechanics-based artificial intelligence and machine learning method, which they say can improve the prediction of cancer outcomes and identify treatment targets using the comprehensive molecular background of individual patients. The approach, described in APL Quantum, addresses a major roadblock to leveraging conventional AI for predicting patient outcomes in clinical trials, namely the vast amounts of data needed to train large language models and to account for the complexity of disease drivers.

“It’s much more than just one gene—everything that’s happening in the cells of the patient matters,” said Orly Alter, PhD, associate professor of biomedical engineering at the University of Utah’s Scientific Computing & Imaging Institute. To take this into account, the team developed a method that is capable of analyzing multiple layers of molecular information simultaneously, including tumor DNA, blood DNA, and tumor RNA.

Clinical trials can enroll as few as 20 to 100 patients, while existing genomic datasets often contain data detailing millions to billions of molecular features. According to the researchers, many existing AI and machine-learning methods need more patient samples than genetic features to properly train the model. For instance, they pointed to a recent large language model of the 30,000-nucleotide genome of the COVID-19 virus, which needed 110 million samples. Extrapolating from this, the Utah team said that a complete modeling of the three billion nucleotides in the human genome would require 33 trillion patient samples.

To overcome this constraint, the investigators used a collection of algorithms known as multitensor comparative spectral decompositions, which Alter developed based on the quantum mechanical concepts of entanglement and superposition. The result, the team said, is analogous to a prism splitting light into its individual color components, providing data on multiple layers of a patient’s molecular makeup, including tumor and blood genomes and RNA transcriptomics, able to demonstrate linked patterns in cancer that can predict individual patient outcomes.

“The model rewrites a set of multiple omic profiles from one patient as a superposition of phenotypes, each represented by a set of multiple entangled patterns,” the researchers wrote. Importantly, data from one molecular profile can approximate an analysis from other profiles, which allows predictions to remain consistent among different types of biological data.

The researchers tested their model using an open-source dataset of the childhood cancer neuroblastoma. Their analysis found two previously unrecognized predictors of survival and treatment response, with each predictive element found in three separate, but interconnected data types: tumor genomes, blood genomes, and tumor transcriptomes. The study found that these predictors outperformed the currently used biomarker, the MYCN gene, for predicting treatment response and outcomes.

The new method builds on the substantial body of work by Alter and colleagues. Earlier research in this area had used related comparative spectral decomposition methods to analyze genomic and transcriptomic data in other tumor types, including glioblastoma.

The team will continue its work as it looks to develop an approach that can be used in the clinic. “That’s the ultimate precision medicine,” Alter said. “You have a single person. Can you take the data from just that one person and come up with a treatment for them? I think we can get there.”

The post Quantum Mechanics Principles Help Researchers Build Cancer Prediction Model appeared first on Inside Precision Medicine.

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Solid Tumor CAR-T Therapy Approved in China, a World First

In a landmark advance for cellular immunotherapy, CARsgen Therapeutics has received regulatory approval in China for satricabtagene autoleucel (satri-cel; CT041), the first CAR-T cell therapy globally approved for the treatment of a solid tumor.

The National Medical Products Administration (NMPA) of China approved satri-cel for Claudin18.2 (CLDN18.2)-positive, HER2-negative advanced gastric or gastroesophageal junction adenocarcinoma (G/GEJA) patients who have progressed after two prior lines of therapy. The decision is a turning point for the CAR-T field, which has improved hematologic malignancies but has struggled to overcome solid tumor biological barriers.

The approval addresses a major unmet need in gastric cancer, the fifth most commonly diagnosed cancer and the fifth leading cause of cancer-related death worldwide, with more than one million new cases and over 750,000 deaths annually. East Asia, particularly China, accounts for 40% of global cases due to risk factors like Helicobacter pylori infection, dietary exposures, and an aging population.

Despite advances in chemotherapy, targeted therapy, and immune checkpoint inhibitors, advanced gastric cancer patients have poor outcomes, especially after multiple treatment lines fail. CAR-T therapy first entered clinical testing for solid tumors in the late 1990s and early 2000s, with pioneering studies targeting ovarian cancer and later neuroblastoma and colorectal cancer, laying the groundwork for today’s next-generation cell therapies.

Satri-cel is an autologous CAR-T therapy that targets CLDN18.2, a stomach-specific tight-junction protein that is highly expressed in gastric and pancreatic cancers but has limited expression in normal tissues. The therapy uses a humanized anti-CLDN18.2 CAR construct that is linked to CD28 and CD3ζ signaling domains, which allows for targeted elimination of tumor cells.

The program’s CARsgen preconditioning strategy boosts CAR-T activity in the immunosuppressive solid tumor microenvironment. Patients receive low-dose nab-paclitaxel to increase CAR-T cell infiltration and antitumor efficacy in addition to cyclophosphamide and fludarabine lymphodepletion.

Clinical evidence supporting approval comes from a randomized confirmatory study published in The Lancet in 2025. In heavily pretreated patients with advanced G/GEJA, satri-cel demonstrated clinically meaningful efficacy and a manageable safety profile compared with available treatment options. The results provide one of the strongest demonstrations to date that CAR-T therapy can generate meaningful clinical benefit in solid tumors.

Importantly, CARsgen is already aggressively pursuing a development strategy beyond late-line gastric cancer. Currently, there are Phase Ib studies in advanced gastric, gastroesophageal junction, and pancreatic cancers, a confirmatory Phase II study in advanced G/GEJA, a Phase Ib study evaluating satri-cel as adjuvant therapy in pancreatic cancer, and investigator-initiated studies evaluating adjuvant and first-line sequential therapy. Satri-cel is being tested in a Phase Ib/II trial for advanced gastric and pancreatic adenocarcinoma outside China, demonstrating its global development goals.

The program has also been the subject of considerable regulatory attention. The FDA has designated satri-cel for CLDN18.2-positive gastric and gastroesophageal junction cancers as an RMAT and Orphan Drug. In Europe, the therapy has been awarded Orphan Medicinal Product designation and PRIME status by the European Medicines Agency. In China, the NMPA designated this product a Breakthrough Therapy for advanced gastric or gastroesophageal junction cancer patients who had failed at least two lines of treatment.

Satri-cel may be the first CAR-T therapy to clear the regulatory finish line in solid tumors, but the competition is heating up. Several companies are developing CLDN18.2-targeted CAR-T, T-cell engager, and antibody programs. AstraZeneca’s zolbetuximab franchise validated CLDN18.2 as a gastric cancer therapeutic target, and Chinese and U.S. biotech companies are developing cell therapy programs to replicate or improve on satri-cel’s results.

For cancer specialists and cell therapy specialists, satri-cel’s approval is not just a new treatment option but a proof-of-concept that engineered cellular therapies can successfully address the challenges of solid tumors. Whether this breakthrough can be applied to other tumor types remains to be seen, but the field has crossed a milestone that has eluded oncology for decades.

The post Solid Tumor CAR-T Therapy Approved in China, a World First appeared first on Inside Precision Medicine.

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Manchester Met wins funding to boost AI health innovation

Manchester Metropolitan University will promote AI for business and the development of wearable health technologies through funding awarded by UK Research and Innovation’s Local Innovation Partnership Fund (LIPF). The funding will support two initiatives: Grow AI, which aims to accelerate AI adoption among businesses, and GM-WIC, which will bring together the NHS, universities, businesses and […]
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Dynamic changes of gut microbiota during progression of three Alzheimer’s disease mice models

IntroductionAlzheimer’s disease (AD) is an age-related and progressive neurodegenerative disorder characterized by cognitive impairment and irreversible neuronal degeneration, affecting approximately 55 million individuals worldwide. Despite extensive research efforts, the underlying pathogenic mechanisms of AD remain incompletely understood, and effective therapeutic strategies for preventing or delaying disease progression are still lacking. Increasing evidence suggests that the microbiota-gut-brain axis plays an important role in neurodegenerative diseases, including AD. However, the dynamic alterations of gut microbiota during AD progression across different transgenic mouse models remain poorly characterized.MethodsIn the present study, we investigated age-dependent changes in gut microbiota composition in three commonly used AD mouse models, including APP/PS1, 3xTg, and 5xFAD mice, using 16S rRNA gene sequencing. Fecal samples were collected longitudinally at 2, 4, 6, and 8 months of age to evaluate microbial diversity, community structure, and differential bacterial taxa during aging and disease progression.ResultsOur results demonstrated distinct and model-dependent alterations in gut microbiota composition across different stages of AD progression. Significant changes in microbial diversity and bacterial community structure were observed among the three AD mouse models and wild-type controls. In particular, dynamic alterations in Verrucomicrobiota, Proteobacteria, and Actinobacteriota were consistently identified during aging in AD mice. In addition, β-diversity, Linear discriminant analysis effect size (LEfSe), and correlation network analyses further revealed differential microbial signatures associated with different AD mouse models and age stages.DiscussionOverall, our findings provide additional evidence that gut microbiota composition undergoes dynamic alterations during aging in multiple AD mouse models and may be associated with AD-related progression. This study may contribute to a better understanding of microbiota-associated changes during AD development and provide a basis for future mechanistic studies targeting the microbiota-gutbrain axis in AD.
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Applications of machine learning algorithms to detect digital addiction: a meta-analysis

Digital addiction (DA) has emerged as a significant global concern, yet traditional diagnostic methods relying on self-report questionnaires face subjective bias and threshold inconsistencies. Recent advances in machine learning (ML) offer promising alternatives for automated DA detection. This study conducted a systematic meta-analysis of 64 eligible studies (75 independent datasets; N = 165,624), employing both single-group proportion and bivariate diagnostic test accuracy (DTA) models. The pooled classification accuracy was 0.87 (95% CI [0.85, 0.90]), and the DTA framework yielded a robust AUC of 0.92, with balanced sensitivity and specificity (both 0.86). Subgroup analyses showed high accuracy across subtypes, particularly for internet (0.90) and social media addiction (0.86). Accuracy was comparable between survey-based and physiological data, though physiological markers demonstrated superior specificity (0.90). These findings underscore the potential of ML-driven tools as scalable screening instruments while emphasizing the need for representative sampling and standardized diagnostic criteria to advance digital mental health practice.
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The impacts of a justice-focused body image program for early adolescents

To address gaps in universal, diversity-focused eating disorders prevention with early adolescents, our team co-created an evidence-informed body image intervention through a community-engaged, participatory research process. The Body Justice intervention and associated research were co-created by a team of middle school students and staff and undergraduate students and faculty in the Pacific Northwest United States. The intervention includes eight brief lessons (six hours total) with culturally-tailored content rooted in cognitive dissonance and media literacy (e.g., cultural appearance ideals, diversity representation within media, food culture). The intervention was delivered with 7th grade students over three years (N = 333; 49% students of color; 53% cisgender boys, 36% cisgender girls, 12% gender diverse; 27% sexually diverse) using college student leaders (near peers) and middle school student co-leaders. Student satisfaction immediately after the intervention was moderate overall and higher for students of color, sexually diverse students, and cisgender girls and gender diverse students compared to their peers with majority identities. Across the sample, there was a significant reduction in unhealthy weight control behaviors from baseline to two-month follow-up with similar improvement among subgroups except for students of color, who had smaller reductions over time compared to their white peers. Across the sample, there was a significant reduction in internalized appearance norms from pre to post-intervention and through follow-up. These reductions were similar across gender, but the change was significant only for white students and straight students. There was no overall improvement in perceived appearance pressure from social media over time, but subgroup analyses revealed that students of color experienced improvement over time unlike other subgroups. In general, subgroup analyses should be interpreted cautiously due to concerns about adequate power. These results suggest that the Body Justice curriculum was delivered effectively and was well-liked by middle school students with marginalized identities. While aspects of the intervention were beneficial (e.g., a reduction in unhealthy weight control behaviors over time), findings suggest potentially differential results across identity subgroups. This has implications for collaborative school-based research, body image and eating disorders prevention, and community-engaged methods to foster equity.
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Gender-specific symptom outcomes on cariprazine treatment: a 12-month naturalistic longitudinal follow-up study in schizophrenia

IntroductionA growing body of literature is focusing on third-generation antipsychotics and their unique characteristics, but few studies have examined gender as a crucial factor in response profiles. The present study aims to address this gap by analyzing the outcomes of 12-month naturalistic treatment with cariprazine to elucidate changes in specific psychopathological domains between men and women.MethodsThe present 12-month longitudinal naturalistic study involved a sample of individuals diagnosed with schizophrenia according to the DSM-5-TR treated with cariprazine at the outpatients’ psychiatric services of a major university and community hospitals in Italy. The assessments conducted included sociodemographic data, the Structured Clinical Interview for the DSM-5 (SCID-5), and the Positive and Negative Symptom Scale (PANSS) Total and Subscale scores, as well as the PANSS-derived Marder factors. The PANSS was administered at three time points: before starting the treatment with cariprazine (T0), after 6 months (T1), and after 12 months (T2).ResultsFifteen male and 17 female subjects were assessed at the three time points. The mean dose of cariprazine was 4.2 ± 1.3 mg for men and 4.0 ± 1.5 mg for women. Both genders exhibited improvements in all PANSS subscale symptoms after 6 and 12 months of cariprazine treatment compared to the baseline, with the only exception of the Uncontrolled hostility/excitement Marder factor among men. Progressive improvements through time points in symptom subscales were found in both sexes, reaching numerical differences in every PANSS subscale in both sexes at T2. Gender specifc response profiles emerged after 6 and 12 months of treatment in the PANSS subscales and items in men and women.DiscussionCariprazine exhibited significant efficacy in both sexes, with no significant differences between men and women despite a gender specific response profile emerged. Additional studies are needed to further investigate the efficacy profile and long-term outcome of cariprazine treatment by gender.
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TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity

ObjectivesThis study aimed to develop an interpretable multimodal framework for detecting tic events in children with tic disorders by translating model decisions into structured, time-aligned evidence from synchronized video and physiological signals.MethodsTIC-XNet was developed to jointly analyze synchronized video, heart rate, and electrodermal activity signals and to generate structured evidence outputs. Recordings from 417 children with clinically diagnosed tic disorders were collected during structured clinical assessments and home-based observations. TIC-XNet was compared with a prediction-only black-box model and a post-hoc explainable model under matched predictive backbones and identical training settings.ResultsWithin the evaluated internal subject-level split, TIC-XNet achieved the best performance on the pooled shared test set, with a window-level AUC of 0.915 ± 0.019, higher event-level recall and precision, fewer missed events, and lower post-buffering prediction latency than the comparator models. Its translated outputs also showed higher decision fidelity, greater stability under perturbation, and closer temporal alignment with expert-annotated tic onsets. Subject-level translated numerical signals were associated with tic severity.ConclusionsThese findings indicate that evidence translation can support more interpretable multimodal detection of tic events in children with tic disorders while remaining compatible with strong predictive performance.
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STAT+: Gene-editing startup launches with $230 million and a Chinese licensing deal

A new gene-editing startup has threaded the needle of negotiating contracts with a Chinese drug company, raising $230 million in funding, and executing a reverse merger with a preexisting biotech company. 

The result? Serapha Bio, which launched Tuesday to develop a one-and-done treatment for a liver and lung disease called Alpha-1 Antitrypsin Deficiency, or AATD, founding investors RA Capital and RTW Investments told STAT exclusively. 

RA and RTW arranged $138 million in Series A funding for Serapha, along with $92 million in additional funds tied to the company’s reverse merger with Boundless Bio. Boundless Bio launched in 2019 to study extrachromosomal DNA and its role in cancer growth but ran into trouble with its lead program.

Continue to STAT+ to read the full story…