Tag: Academic research

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Antibody-Drug Conjugate Shows Activity in Hard-to-Treat Uterine Cancer

A Phase II clinical trial led by researchers at Yale School of Medicine and Yale Cancer Center has found that the antibody-drug conjugate sacituzumab govitecan, also known as Trodelvy, demonstrated encouraging clinical activity in patients with recurrent uterine cancer who had already exhausted several standard treatment options.

The findings, published in Clinical Cancer Research, suggest the therapy could become an important new option for patients with advanced endometrial cancer after chemotherapy and immunotherapy stop working.

A difficult disease to treat after relapse

Endometrial cancer is the most common gynecologic cancer in the United States, and rates continue to rise worldwide. While recent advances in immunotherapy have improved treatment for some patients, options remain limited once the disease returns after platinum-based chemotherapy or checkpoint inhibitor therapy.

Patients with recurrent disease often face poor outcomes, particularly those with aggressive tumor types such as uterine serous carcinoma and carcinosarcoma. Standard second-line chemotherapies typically produce modest response rates and short-lived disease control.

Researchers therefore wanted to investigate whether sacituzumab govitecan could improve outcomes in this challenging setting.

How the drug works

Sacituzumab govitecan belongs to a newer class of targeted therapies known as antibody-drug conjugates, or ADCs. These drugs combine an antibody that recognizes cancer cells with a chemotherapy payload designed to destroy them.

In this case, the therapy targets Trop-2, a protein commonly overexpressed in several aggressive cancers, including many uterine tumors. Attached to the antibody is SN-38, the active metabolite of irinotecan, a well-known chemotherapy drug.

By delivering chemotherapy directly to Trop-2–expressing cancer cells, researchers hope to increase anti-tumor activity while limiting damage to healthy tissue.

The drug is already approved for metastatic breast cancer and urothelial cancer, but remains investigational in uterine cancer.

Trial included heavily pretreated patients

The study enrolled 50 patients with recurrent or persistent endometrial cancer between 2020 and 2024. All participants had previously received platinum-based chemotherapy, and many had also undergone treatment with immune checkpoint inhibitors such as pembrolizumab or dostarlimab.

The study population represented a particularly difficult-to-treat group. Most patients had aggressive tumor histologies, including serous carcinoma, carcinosarcoma, or grade 3 endometrioid disease. Patients had received a median of two prior treatment regimens, with some undergoing as many as four lines of therapy before entering the study.

Encouraging responses and survival data

The trial met its primary endpoint, achieving an objective response rate of 28%. Two patients experienced complete responses with no detectable cancer remaining, while another 12 patients achieved partial responses with substantial tumor shrinkage.

In total, more than 70% of evaluable patients experienced some degree of tumor reduction during treatment. The study also reported durable responses, with a median response duration of 9.3 months. Several patients were still responding at the time of analysis.

Median progression-free survival reached 5.5 months, while median overall survival was 17.5 months in this heavily pretreated population.

Researchers also noted that responses were observed across multiple tumor subtypes rather than being limited to one specific histology.

“The results of our Investigator Initiated Trial complement and extend the TROPiCS-03 Trial results by demonstrating significant clinical activity of SG not only against the most common histological types of uterine cancer (endometrioid tumors) but also in patients harboring biologically aggressive endometrial tumors such as uterine serous carcinoma and carcinosarcoma,” said Alessandro Santin, MD, the study’s lead author.

Side effects remained manageable

As expected with potent cancer therapies, treatment-related side effects were common. The most frequent severe toxicities included neutropenia, anemia, fatigue, diarrhea, and febrile neutropenia.

However, investigators reported that most adverse events were manageable using supportive care measures such as growth factor support, anti-diarrheal medications, hydration, and dose adjustments. No treatment-related deaths were reported during the trial.

Looking ahead

The researchers cautioned that the study was relatively small and lacked a randomized comparison arm. Nevertheless, the results add to growing evidence supporting sacituzumab govitecan in advanced endometrial cancer, particularly for patients who have limited options remaining after standard therapies fail.

A larger international Phase III study is already underway to compare the drug directly against standard chemotherapy in patients with recurrent endometrial cancer following platinum chemotherapy and immunotherapy.

The team also highlighted future possibilities for combining the therapy with immunotherapy approaches, particularly because the drug’s chemotherapy payload may help stimulate anti-tumor immune responses.

“This is a major bench-to-bedside accomplishment for patients with uterine cancer,” Santin said.

 

The post Antibody-Drug Conjugate Shows Activity in Hard-to-Treat Uterine Cancer appeared first on Inside Precision Medicine.

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Exchange Marketplace Launched to Help Stalled Cell and Gene Therapies

A type of exchange marketplace for cell and gene therapies has launched to try and reinvigorate and relaunch candidate therapies that are no longer being developed due to financial constraints, despite having good science behind them.

CGTxchange, an online platform enhanced by artificial intelligence, is the brainchild of the American Society of Gene and Cell Therapy (ASGCT) and the Orphan Therapeutics Accelerator. The latter was set up in 2024 as a patient‑centered, non‑profit biotech accelerator that acquires “shelved” clinical‑stage therapies for ultra‑rare diseases and completes their development and commercialization so patients can actually access them.

The new platform was launched at the ASGCT conference in Boston and is designed to help stalled assets to find a new home. This is designed to address funding pull‑backs, program terminations, and company exits, which are forcing hundreds of cell and gene therapy programs to be discontinued or indefinitely paused despite having good scientific promise.

CGTxchange will link up companies who want to develop their assets with funders and partners who are looking for new development opportunities in cell and gene therapy.

“The cell and gene therapy field has built an extraordinary base of clinical evidence, and yet too many of those programs sit on the shelf for reasons that have nothing to do with the science,” said Terry Flotte, MD, dean of University of Massachusetts Chan Medical School and president of the ASGCT, in a press statement.

“CGTxchange gives our community a structured way to bring those programs back into view, and to connect them with the partners and funders who can help reactivate them.”

The platform is built so that people who own assets they want to develop can securely upload information about them into a database that is then searchable by possible interested parties such as investors, nonprofits, biotechs, and academic groups. They will have to pay a fee for this service, but the press statement says, “up to 10 of the initial listings will be eligible for discounted onboarding.”

When a match is achieved then the asset owner and the interested party will come to some sort of agreement on further financing. This could include traditional or alternative development and financing models, including nonprofit and hybrid structures.

The platform can theoretically be used by people from around the world, but users must apply and be approved rather than simply signing up. According to the platform’s terms and conditions on its website, “access is restricted to authorized representatives of biotechnology companies, academic institutions, nonprofits, and accredited investors.”

The post Exchange Marketplace Launched to Help Stalled Cell and Gene Therapies appeared first on Inside Precision Medicine.

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Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Background: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines. Objective: The purpose of this study is to develop a clinically interpretable model that classifies the risk levels of T1D complications—acute, chronic, and low—using real-world data and expert clinical rules derived from the Saudi Diabetes Clinical Practice Guidelines. Methods: A pediatric T1D dataset comprising of 306 patients was preprocessed through structured cleaning and feature engineering. Risk labels were constructed using Saudi Diabetes Clinical Practice Guidelines–derived rules. Feature selection was performed using a hybrid approach that combined the SHAP (Shapley Additive Explanations) analysis with exhaustive feature selection. A decision tree model was trained and optimized via cross-validation, using the -score as the primary performance metric. Results: The final model achieved a high mean -score of 0.9876 with a low variance of 0.0189, using only 5 clinical features: BMI, hypoglycemia, disease duration, hemoglobin A, and impaired glucose metabolism. These features were consistently ranked as the most influential. The resulting decision tree offered a transparent logic path, enhancing its clinical interpretability and usability. Conclusions: This study demonstrates that a simple and interpretable model, guided by national clinical guidelines, can effectively predict the risk levels of T1D complications in children and adolescents. Its strong performance, clarity, and reliance on a small number of clinically meaningful features make it a promising candidate for integration into clinical decision support systems. This supports a shift toward predictive and personalized diabetes care.
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Large Language Models and Their Applications in Mental Health: Scoping Review

Background: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. Objective: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. Methods: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. Results: In total, 41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
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A Novel Haptic Cardiac Simulator: Mixed Methods Pilot Evaluation in Medical Students and Educators

Background: Cardiac auscultation is an essential component of clinical examination but is often challenging to achieve proficiency in. Self-contained, multisensory learning resources that incorporate simultaneous visual and haptic stimuli offer a unique approach to supporting learners in acquiring this core skill. Objective: This pilot study of both medical students and clinical educators evaluated the utility of a novel iPhone app, Haptic Heart, which generates haptic vibrations to simulate heart sounds and murmurs. We aimed to explore the perceptions of students and educators when using haptics as a learning resource and the underlying reasons behind these perceptions and to gather lessons that would inform future development of the resource. Methods: Clinical-year medical students from the Lincoln Medical School with access to an iPhone were invited to trial Haptic Heart between October 2023 and December 2024. Cardiology specialists involved in clinical education were also invited to take part. After using the app, participants were asked to complete a modified version of the 12-item Evaluation of Technology-Enhanced Learning Materials: Learner Perceptions questionnaire that included additional free-text items. Educators were also asked to comment on the resource’s authenticity and perceived usefulness. Quantitative responses were analyzed using descriptive statistics; free-text responses were analyzed for common themes. Results: A total of 21 students and 18 educators completed the evaluation. Both cohorts returned positive responses across nearly all questionnaire items, with students showing near universal agreement that the app was of excellent quality (21/21, 100%), supported their learning needs (21/21, 100%), and would change their clinical practice (20/21, 95.2%). Educators similarly rated the resource highly for learning utility (16/18, 88.9%) and authenticity (13/18, 72.2%). Reported technical difficulties were minimal for students (1/21, 4.8%) and educators (2/18, 11.1%). Analysis of free-text responses suggested that learners valued the ability to “feel” murmurs and to vary heart rate. Educators highlighted the resource’s novelty and innovation, although some noted concerns about audio quality when using a stethoscope to auscultate haptic vibrations directly. Conclusions: This pilot evaluation demonstrates the potential of smartphone-based haptic technology as a tool for medical education. Haptic Heart was perceived by both students and educators as an innovative educational tool for cardiac auscultation. Further work should focus on expanding the range of haptic patterns provided and exploring the effectiveness of these resources on learning.
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<![CDATA[Study ties higher schizophrenia rates in Black Americans to neighborhood vulnerability, spotlighting faster early-psychosis care and social supports.]]>
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ASGCT 2026: Beverly Davidson Offers Vehicle and Route for Huntington’s Disease Gene Therapy

BOSTON – Geneticist Beverly Davidson, PhD, received the 2026 Outstanding Achievement Award from the American Society of Gene and Cell Therapy (ASGCT). Davidson is currently the chief scientific strategy officer at the Children’s Hospital of Philadelphia (CHOP) and a former president of ASGCT.

Some of the research Davidson presented was conducted at a new biotech company she co-founded called Latus Bio, which earlier this month announced it had raised $97 million in a Series A round. The company develops novel AAVs to specifically target central nervous system (CNS) disorders, with a lead program in Huntington’s disease (HD).

After thanking her mentors—Bill Kelly, MD, Michael Welsh, MD, and Kathy High, MD—Davidson turned her attention to presenting new advances in engineered gene therapies. Throughout her career, she has focused on improving adeno-associated viruses (AAVs) for CNS gene therapies, with a particular emphasis now on HD. Key elements include selecting the right cargo and developing the appropriate delivery vehicle. Her goal is to scale lab research in neurons, mouse models, and non-human primates (NHPs) to treat patients, including adults with HD.

Major hurdles to tackling genetic diseases of the brain include scalability and a lack of potency, Davidson said. The search for alternative AAV serotypes to AAV2 that could target neuronal cells began back in 2000. IV administration does not provide sufficient targeting to the brain. Even AAVs that have been engineered to enter the brain from the blood have high peripheral exposure and a high cost of goods per patient, which significantly lowers scalability and impact. (In one study, liver biodistribution of AAV was many orders of magnitude higher than in the CNS.)

Davidson focused on HD, the late-onset, dominantly inherited genetic disease. The identification of the gene harboring the HD mutation in the early 1990s by a consortium of researchers was one of the biggest success stories in human genetics. Even more remarkable was the underlying disease mechanism—the expansion in exon 1 of the gene of a triplet repeat sequence (CAG) producing an abnormally long string of glutamine residues in the huntingtin protein.

The right target

One of the major challenges in devising a gene therapy for HD is ensuring that the therapeutic reaches the right network—the deep brain and cortical areas. Therapies have to reach the right circuit, and the right cells in those circuits, Davidson said. Over the years, her group has tailored AAVs for delivery to the brain, inserting peptides into exposed loops of the virion to allow for targeting and unbiased diversity for blood-to-brain delivery. Nowadays, she said, machine learning approaches can be applied for further capsid improvements.

Davidson’s CHOP lab developed a method for screening AAVs with enhanced potency for CNS therapies. After generating huge libraries containing tens of millions of novel capsids, the group performed serial enrichments to identify the most attractive capsids. After screening pools of injected capsids into two species of monkeys, a winning capsid emerged: AAV-DB-3.

Davidson’s group infused AAV-DB-3 into NHPs, looking for targeting to the putamen (base of the forebrain) and caudate regions. Those results were published in Nature Communications in 2025.  “AAV-DB-3 really stood out for its ability to transduce deep layer cortical neurons that are important” in HD, Davidson said. Moreover, the results were achieved with relatively low doses and only required a single infusion per hemisphere, outperforming the widely used AAV5.

Somatic instability

With a promising delivery vehicle identified, Davidson next addressed the therapeutic strategy, which takes aim at the somatic expansion of the CAG repeat. This codon grows longer over time in certain cells in the brain, sometimes expanding to hundreds of repeats.

MSH3 is a DNA repair protein that is required for CAG repeat expansions, as seen in mouse models of HD and other triplet repeat disorders, including myotonic dystrophy. Research led by Paul Ranum, PhD, who is a co-founder of Latus Bio, posted in a preprint on bioRxiv earlier this year, modeled the impact of lowering levels of MSH3 on somatic instability.

Ranum and colleagues used an artificial microRNA showed to lower MSH3 levels in NHPs by 48-94 percent. Computational modeling suggests that this would reduce somatic instability and delay onset of HD symptoms by many years. Early studies using a well-known HD mouse model, the Q111 mouse, to assess biodistribution, quantify knockdowns, and assess the impact on somatic CAG repeat expansion. AAV-DB-3 expression is highest in the striatum and cortex at 16 weeks, dropping MSH3 levels by 50%.

Davidson closed by emphasizing the need to ensure scalability for treatment beyond ultra-rare disorders. Latus hopes to file an Investigational New Drug application for its HD therapy, LTS-201, in the second half of 2026. At least two other biotech companies are also targeting MSH3 by other means.

 

 

The post ASGCT 2026: Beverly Davidson Offers Vehicle and Route for Huntington’s Disease Gene Therapy appeared first on GEN – Genetic Engineering and Biotechnology News.

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Exosome-based therapy for epilepsy: a systematic review and meta-analysis of preclinical studies

ObjectiveThis study aims to quantitatively assess the efficacy of exosome therapy for epilepsy through a systematic review and meta-analysis of preclinical animal experiments. We seek to clarify its overall effects on seizure reduction, cognitive function preservation, and neuroinflammation suppression.MethodsA systematic search was conducted across four English-language and four Chinese databases to include epilepsy animal studies. Continuous outcomes were synthesized using standardized mean differences (SMD) and 95% confidence intervals (CI), with fixed or random effects models selected based on heterogeneity.ResultsA total of eight preclinical studies were included. The overall meta-analysis revealed that exosome treatment significantly reduced the duration of seizures (SMD = −2.30, 95% CI −4.24 to −0.36), decreased the frequency of spontaneous recurrent seizures (SMD = −1.38, 95% CI −2.17 to −0.58), and prolonged the seizure latency (SMD = 1.49, 95% CI 0.08–2.90). In terms of cognitive function, exosomes significantly shortened the escape latency in the Morris water maze (SMD = −1.38, 95% CI −2.17 to −0.58), increased the percentage of time spent in the target quadrant (SMD = 3.69, 95% CI 0.30–7.08), and enhanced the number of platform crossings (SMD = 1.41, 95% CI 0.60–2.21), with no significant changes in swimming speed. Neuropathological analysis indicated that exosome treatment significantly increased the number of hippocampal neurons (SMD = 4.48, 95% CI 1.46–7.49) and markedly reduced levels of glial fibrillary acidic protein (GFAP) (SMD = −3.61, 95% CI −7.08 to −0.14), ionized calcium-binding adaptor molecule 1 (IBA-1) (SMD = −10.27, 95% CI −20.29 to −0.25), tumor necrosis factor-alpha (TNF-α) (SMD = −2.95, 95% CI −4.21 to −1.69), and interleukin-1 beta (IL-1β) (SMD = −7.39, 95% CI −14.64 to −0.13). Although some outcomes exhibited heterogeneity and publication bias, the corrected primary effects remained statistically significant. The source of exosomes, administration route, and dosage may be critical variables influencing their efficacy.ConclusionExosome therapy improves seizure phenotypes and protects cognitive function in epilepsy models by suppressing neuroinflammation to promote neuronal survival, providing evidence for further mechanistic and clinical translation studies.
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Molecular mechanisms of autophagy-lysosomal pathway dysfunction in neurodegenerative diseases and therapeutic strategies for lysosomal repair: a review

The autophagy-lysosomal pathway (ALP) is a critical intracellular protein degradation system responsible for maintaining proteostasis and metabolic balance within cells. Dysfunction of this pathway has been increasingly recognized as a key pathological basis underlying various neurodegenerative diseases (NDs). This review provides a comprehensive overview of the molecular mechanisms by which ALP impairment contributes to defective protein degradation in neurodegeneration. We focus on the impact of lysosomal structural integrity and functional imbalance on cellular fate, highlighting the interplay between protein oxidative damage and degradation system dysregulation. Furthermore, we summarize the current therapeutic strategies aimed at lysosomal repair, evaluating their potential clinical applications and efficacy. By integrating the latest research advances, this review aims to deepen the understanding of the pathological mechanisms of autophagy-lysosomal pathway dysfunction in neurodegenerative diseases, clarify the key molecular targets of lysosomal damage and repair, and provide theoretical basis for target screening and validation and practical reference for the development of targeted drugs for neurodegenerative diseases.
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A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations

ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.