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Astellas and Vir strike prostate cancer deal

Astellas has agreed a US$1.7bn global prostate cancer deal with Vir Biotechnology to develop a masked T-cell engager designed to harness the immune system against the disease.
The collaboration centres on VIR-5500, a PSMAxCD3 bispecific drug known as a T-cell engager, or TCE. TCEs activate immune cells called T cells and direct them to attack cancer cells.
Unlike radioligand therapies, which deliver radioactive material to tumours, TCEs aim to recruit the body’s own defences.
VIR-5500 targets PSMA, a protein found on prostate cancer cells, and uses what Vir calls PRO-XTEN dual-masking technology.
This is designed to keep the drug inactive until it is unmasked within the tumour, which the company says may limit side effects elsewhere in the body.
Vir says only about 30 per cent of patients with metastatic castration-resistant prostate cancer, an advanced form of the disease that no longer responds to hormone-blocking treatment, survive for five years after diagnosis.
Marianne De Backer, chief executive of Vir, said: “The field is moving very, very fast.
“And we believe that we could accelerate the programme by working together with someone who has a really strong track record in the space.”
Updated phase 1 data presented at the 2026 ASCO Genitourinary Cancers Symposium showed that 82 per cent of heavily pretreated patients receiving the highest evaluated doses achieved a 50 per cent or greater reduction in PSA levels, a marker of disease activity. Among patients with measurable tumours, 45 per cent recorded an objective response.
Cytokine release syndrome, an immune overreaction commonly associated with TCEs, was observed in 50 per cent of the 58 patients treated and was described by Vir as generally restricted to mild events.
The company said preventative steroids were not required and no drugs were used to manage severe CRS.
Under the agreement, Astellas will provide US$335m in upfront and near-term payments, including US$240m in cash, US$75m through an equity investment and a US$20m near-term milestone.
Vir is eligible for up to US$1.37bn in additional development, regulatory and sales milestones, along with tiered double-digit royalties on sales outside the US.
Development costs will be shared 60–40, with Vir holding an option to co-promote the drug in the US, while Astellas will be responsible for commercialisation elsewhere.
For Astellas, the deal may strengthen its position in prostate cancer as Xtandi, its partnered drug with Pfizer, begins to lose patent protection in several territories this year.
Vir licensed the underlying technology from Sanofi in 2024, meaning some proceeds from the agreement will be shared with the French pharmaceutical company.
Competition in the field remains strong. Novartis already markets its radioligand therapy Pluvicto, while Janux Therapeutics has a masked TCE that is currently ahead of VIR-5500 in development.
Amgen and Johnson & Johnson are also developing TCE candidates for prostate cancer.
Vir plans to move VIR-5500 into phase 3 trials in 2027 and to begin testing the drug in earlier-stage disease in the second quarter of 2026.
Johann de Bono, the phase 1 trial’s principal investigator and a prostate cancer expert at the Institute of Cancer Research in the UK, said: “It is remarkable to see these early signs of profound anti-tumour activity in heavily pretreated mCRPC patients, and the favourable tolerability with minimal CRS to date means VIR-5500 could play a role in treating earlier disease.”
Anthony Jarkowski, primary focus lead of immuno-oncology at Astellas, added: “What really stood out to us is how they’re balanced, efficacy and toxicity.”
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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