Tag: United Kingdom

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Functional MRI evidence of brain alterations in premenstrual dysphoric disorder: a systematic review

IntroductionPremenstrual Dysphoric Disorder (PMDD) affects approximately 1.6% of women of reproductive age and significantly impacts quality of life. Despite its prevalence, the underlying pathophysiology remains incompletely understood. First-line treatment typically involves selective serotonin reuptake inhibitors (SSRIs); however, approximately 40 percent of women with PMDD do not respond to these medications. This systematic review synthesizes current evidence on functional brain alterations in women with PMDD, as assessed using functional magnetic resonance imaging (fMRI), with the goal of identifying potential novel therapeutic strategies.MethodsData from 598 participants, including 294 PMDD patients and 304 healthy controls, were analyzed.ResultsThe findings suggest alterations in both topdown regulatory mechanisms and large-scale brain networks, including the salience network, default mode network, and executive control network. These alterations are characterized by decreased activation in the anterior cingulate cortex, dorsolateral prefrontal cortex, medial orbitofrontal cortex, and postcentral gyrus, alongside increased activation in the amygdala and insula, as well as impairments in corticolimbic connectivity.DiscussionThese results highlight the complexity of PMDD, implicating widespread neural circuits rather than a single localized dysfunction. Targeting these mechanisms may inform the development of novel interventions for symptom relief.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251174749.
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Opinion: Grail’s multi-cancer early detection trial was negative. But as an oncologist, I see more to this story

At least 12 of my colleagues boarded planes to the American Society of Clinical Oncology conference in Chicago last week to hear Charles Swanton of the Francis Crick Institute in London share results from the world’s first randomized trial on a multi-cancer early detection test.

The trial — a partnership between the U.K. National Health Service and Grail, which makes the Galleri MCED test — randomized 143,000 average English adults aged 50-77 to receive either usual care or MCED testing. The primary endpoint was reduction of stage 3 and 4 cancers across all cancer types.

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CAR T-Cell Therapy Expands Access to Kidney Transplants 

CAR T-cell therapy has enabled two patients who had been waiting for years for a kidney transplantation to find a match. Published today in the New England Journal of Medicine, preliminary results from a Phase I clinical trial show that CAR T therapy can significantly increase the chances of a match for people whose immune system would otherwise reject over 99% of kidneys available for transplant. 

“This is the first demonstration that CAR T cells can be used not only to treat cancer, but also to help patients who previously had no opportunity to receive a compatible donor kidney,” said Ali Naji, MD, PhD, professor of surgery at the University of Pennsylvania and principal investigator of the study. “For patients who have spent years on the kidney transplant waiting list, this approach could be transformative.”

More than 91,000 people in the U.S. are currently waiting for a kidney transplant. Physicians assess each patient’s likelihood of finding a compatible donor using the calculated panel reactive antibody (cPRA) score, which measures how broadly the immune system is primed to attack foreign tissues. For instance, a cPRA score of 90% means the patient is expected to reject 90% of kidneys available for transplant, leaving only a small pool of potential matches available to them. 

The ongoing CTOT-46 clinical trial is recruiting kidney transplant candidates with a cPRA score of 99.5% or higher who have been waiting for a kidney transplant for at least one year. Although the study will continue until up to 20 highly sensitized candidates have been recruited, preliminary results from the first two patients enrolled show promise for this innovative approach. 

Both participants were treated with a dual CAR T therapy designed to remove the immune cells responsible for making antibodies that lead to organ rejection after a kidney transplant. The CAR T cells are engineered to target the CD19 and BCMA proteins, leading to the depletion of memory B cells and antibody-producing plasma cells, respectively. This strategy significantly reduced the levels of circulating antibodies produced by these cells, enabling both patients to receive kidneys from donors that would have previously been incompatible.

One of the patients had been diagnosed with focal glomerulosclerosis at age 14, leading to kidney failure at a young age. His cPRA score had reached nearly 100% after his immune system rejected a second kidney transplant, making a third transplantation next to impossible. After years of waiting for an extremely unlikely match, the CAR T cells lowered his antibody levels enough to find a match within months.

Importantly, the depletion of immune cells was temporary, and the population of B cells and plasma cells returned to normal levels over time. Neither of the patients has shown signs of organ rejection or antibody rebound months after transplantation. Additionally, no cases of severe cytokine release syndrome or neurotoxicity were reported—two common complications of CAR T-cell therapy.

“In this early trial, the CAR T-cell treatment was tolerated well, with no severe side effects, and the immune system began to recover as expected,” said Robert Montgomery, MD, PhD, chair of the department of surgery at NYU Grossman School of Medicine and director of the NYU Langone Transplant Institute. “This early success reflects what cell therapy can do for transplant medicine and opens up new options for patients that could save thousands more lives every year.”

The post CAR T-Cell Therapy Expands Access to Kidney Transplants  appeared first on Inside Precision Medicine.

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AI Model Predicts Radiation Uptake Before Treatment in Advanced Prostate Cancer

A machine learning-based model that integrates imaging uptake features, radiomics, and biomarkers accurately predicts how much radiation is absorbed by patients undergoing prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC).

“One of the biggest challenges in radioligand therapy is that patients can receive very different radiation doses despite being prescribed the same treatment activity,” said Amit Nautiyal, PhD, scientist and National Institute for Health and Care Research fellow at University Hospital Southampton and the University of Southampton in the U.K.

“Our findings suggest that information already available before treatment, such as 18F-PSMA PET/CT imaging and routine clinical biomarkers, may help predict how radiation will be distributed within tumors and healthy organs.”

Nautiyal told Inside Precision Medicine that, in the future, the model “could support more personalized treatment planning, helping to maximize radiation delivery to tumors while minimizing unnecessary radiation exposure to healthy tissues. Ultimately, the goal is to improve treatment effectiveness while reducing the risk of side effects.”

At present, the only way to determine how much radiation has been absorbed by the tumor and surrounding organs such as the kidneys and salivary glands is to use post-treatment imaging and dosimetry calculations, which can be time-consuming and resource intensive.

“Our approach aims to use information already available before treatment, such as positron emission tomography/computed tomography (PET/CT) scans and routine clinical data, to estimate likely absorbed doses before therapy begins,” said Nautiyal.

He and his team integrated 18F-PSMA PET/CT uptake data (total lesion uptake, tumor-to-organ ratios), radiomics features (Gray-Level Co-Occurrence Matrix), and biomarker information (estimated glomerular filtration rate) into a machine learning-based hierarchical mixed-effects model to provide pretherapy predictions of absorbed dose in tumors and organs at risk during ¹⁷⁷Lu-PSMA RLT.

The model, which was presented at the Society of Nuclear Medicine and Molecular Imaging 2026 Annual Meeting, incorporated data from nine patients with mCRPC referred for ¹⁷⁷Lu-PSMA RLT, contributing 57 tumors, 36 salivary glands, and 18 kidneys for analysis.

At the end of cycle 1, ¹⁷⁷Lu-PSMA dosimetry showed that the mean absorbed dose was 11.0 Gy for tumors, 1.8 Gy for salivary glands, and 3.9 Gy for kidneys.

For tumors, the models achieved a mean absolute error (MAE) of 3.2 Gy for the prediction of absorbed dose, meaning that, on average, the predicted tumor dose differed from the measured dose by approximately 3.2 Gy.

By comparison, the MAE was 0.3 Gy for salivary glands and 0.1 Gy for kidneys.

“Given the biological complexity of metastatic prostate cancer and the relatively small study cohort, we consider this an encouraging result,” said Nautiyal, “Tumor dose prediction is inherently challenging because different tumor lesions can behave quite differently, even within the same patient. By contrast, organs such as the kidneys and salivary glands generally exhibit more consistent uptake patterns, which likely contributed to the higher predictive accuracy observed.”

The Bayesian R² values, which indicate how much of the variation in absorbed dose can be explained by the model, were 0.73 for tumors, 0.93 for salivary glands, and 0.99 for kidneys.

The researchers also calculated the 95% Highest Density Interval (HDI) for the model, which indicates whether the uncertainty estimates produced by the model are realistic. The HDIs were 0.89, 1, and 1, for tumors, salivary glands and kidneys, respectively, meaning that, for tumors, about 89% of observed absorbed doses fell within the range predicted by the model.

“This suggests that the model is not only making reasonable predictions but is also providing realistic estimates of how confident it is in those predictions, said Nautiyal. “This is particularly important in healthcare, where understanding uncertainty is often as important as the prediction itself.”

The researchers say that, taken together, the findings support the robustness of the model. They also carried out a leave-one-patient-out analysis, which showed that performance remained stable even when individual patients were excluded from model development and then used for testing.

“This suggests that the model is learning broader patterns rather than simply memorizing the training data,” noted Nautiyal.

Although the results are promising, the researchers acknowledge that this was an early proof-of-concept study and further work is needed before the model can be used routinely in clinical practice.

They now plan to evaluate the model in larger patient populations from multiple centers in the U.K., perform independent external validation, and investigate how predicted absorbed doses correlate with clinical outcomes.

Nautiyal concluded: “If future studies continue to show promising results, predictive tools of this type could eventually support treatment planning and patient stratification in molecular radiotherapy. The aim is to help clinicians make more informed treatment decisions before therapy begins and move towards more personalized radioligand therapy.”

The post AI Model Predicts Radiation Uptake Before Treatment in Advanced Prostate Cancer appeared first on Inside Precision Medicine.

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Web App Helps Flag Antibodies Where Manufacturability Might Be an Issue

Researchers have developed an open-source web app to help drug manufacturers and developers identify unstable antibodies prone to aggregation. The team from Oxford University says the Therapeutic Antibody Profiler 2 (TAP2) can compare the fragment variable component of a proposed antibody to successful clinical-stage antibodies.

According to Clare Gillis, a researcher in bioinformatics and computational biology, the app has the potential to help companies begin process development. “It can help them if they already know their antibody binds as they want, but they need to know if it will pass through the whole developability and manufacturability pipeline,” she says.

TAP2 uses five easily calculable physiochemical metrics based on surface residues of the antibody, Gillis says. These are more likely to affect manufacturability.

The web app metrics are selected to model aspects of antibody behavior, such as hydrophobicity, she adds. If there are big patches of hydrophobic residues on the outside of the antibody, then it’s more likely to be reactive and, thus, less likely to remain stable as a formulated drug product.

Likewise, Gillis explains, if the surface of the antibody features large patches of positive or negative charge, it is likely to have nonspecific reactions that will cause destabilization and aggregation.

With the TAP2 app, companies can flag early amber or red warnings for antibodies where manufacturability might be an issue. In addition, the group also offers a web app profiler for therapeutic nanobodies, TNP, as well as Humatch, an app that can help tweak antibodies to be more ”human-like” and less likely to cause immune reactions in patients, she says.

About Humatch, Gillis says, “you can add a best single point mutation and then iterate over and over until the model believes the antibody is fully humanized.” The app works for any antibody with paired heavy and light chain variable domains (VH and VL), she says, and can potentially help manufacturers of harder-to-produce products that don’t exist in nature.

The post Web App Helps Flag Antibodies Where Manufacturability Might Be an Issue appeared first on GEN – Genetic Engineering and Biotechnology News.