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Category: biotech/medical – Page 187

Efficacy and safety of durcabtagene autoleucel in a phase 1 trial for patients with relapsed/refractory multiple myeloma

Prolonged manufacturing times for autologous CAR T cell therapies can be incompatible with rapidly progressive disease (PD), resulting in increased need for bridging therapy to achieve disease stabilization. Bridging therapy was required for most patients receiving cilta-cel and ide-cel in clinical trials (75 and 87%, respectively) (7, 9, 11, 12). Although use of bridging therapy may not affect ORR, CRR, or PFS, it is associated with worse overall survival (15). Similarly, as wait times for CAR T cell product increase, so does risk of mortality as effectiveness of the therapy decreases (16, 17), highlighting the need for improved CAR T cell products with faster and more reliable manufacturing.

Another issue associated with traditionally manufactured CAR T cell products is T cell exhaustion due to extended periods of in vitro stimulation and expansion during manufacturing (18). Higher levels of exhausted T cells were also observed in the leukapheresis material and final products from patients who later experienced PD (18). T cell exhaustion can result in poor persistence of CAR T cells in the body, thereby impeding function as the proliferation and survival of transferred T cells strongly correlate with their antitumor activity (19–22). Specific T cell populations have varying abilities to expand and persist in vivo. Memory (CD8+CD45RO−CD27+) and naive T cell (TN cell) subsets are associated with improved clinical response, given their ability to proliferate and persist after infusion, whereas effector T cell subsets comparatively exhibit lower self-renewal and survival capabilities (19, 23, 24). Although these patient-specific parameters are initially established in leukapheresis material, preservation of such cell populations in the final product via manufacturing techniques may improve the antitumor activity of a patient’s CAR T cell therapy (18, 19, 23, 24).

Durcabtagene autoleucel (PHE885) is an autologous, BCMA-targeting CAR T cell therapy carrying a CAR construct with a fully human anti-BCMA single-chain fragment variable (scFv) fused to 4-1BB/CD3ζ signaling domains manufactured on a next-generation platform. Prior work has shown that this platform can successfully manufacture product in fewer than 2 days by eliminating the need for ex vivo expansion, thereby preserving overall T cell stemness (the ability of T cells to self-renew and mature), which results in a final product with greater proliferative potential and fewer exhausted T cells (18). Here, we present the findings of part A of the phase 1 study (NCT04318327) of durcabtagene autoleucel in r/r MM, along with correlative analyses of the product before and after infusion.

Electrical pulses reverse aging in sea squirts, offering clues for extending human longevity

A tiny sea creature might hold the secret to reversing the aging process. When treated with a brief series of electrical pulses, sea squirts experience dramatic and long-lasting health improvements that can significantly extend their lifespans, according to a new study by researchers at Stanford and other institutions.

The findings, published in PNAS, open new possibilities for protecting marine species from warming waters, learning what causes stem cells in our own bodies to degrade, and potentially finding new ways to use these cells to treat medical conditions.

“This treatment recharges stem cells,” said study co-senior author Ayelet Voskoboynik, an assistant professor of biology in the Stanford School of Humanities and Sciences. “Understanding this mechanism is the key to unlocking how we might one day slow stem cell aging and trigger rejuvenation pathways.”

How bacteria survive with almost no oxygen— and why blocking one enzyme could aid new antibiotics

Researchers in Leiden have, for the first time, observed how a specialized enzyme helps bacteria stay alive when oxygen levels are low, and how that process can be blocked. The study, published in Science Advances, opens up new possibilities for targeted antibiotics.

It really exists: a secret trick that allows bacteria to survive with very little oxygen. This also applies to bacteria that can make us sick. “Like us, these bacteria need oxygen to survive,” says Ph.D. candidate Tijn van der Velden. “But unlike humans, they have a special enzyme called cytochrome bd that allows them to keep producing energy even when oxygen levels are very low.” Because the enzyme is so important for bacterial survival, it is a promising target for new antibiotics, including potential treatments for tuberculosis.

Immune ‘energy signature’ linked to tuberculosis may explain why some individuals control infection

Researchers at Trinity College Dublin have identified key differences in how immune cells generate and use energy, a process known as cellular metabolism, in people with latent versus active tuberculosis (TB). The findings offer new insights into why some individuals control infection while others develop disease.

The study, published in the Journal of Infection, focused on circulating monocytes, key immune cells involved in the defense against TB infection. The researchers found that cells from people with latent TB remain metabolically flexible, allowing them to mount strong antibacterial responses, whereas cells from people with active TB disease show impaired metabolism and weaker responses to infection.

TB remains the world’s leading infectious killer, with 10.8 million cases and 1.25 million deaths recorded globally in 2023. While many people infected with Mycobacterium tuberculosis never become ill, researchers still do not fully understand why some individuals progress to active disease while others successfully control the infection. The findings could help pave the way for improved TB monitoring tools and future therapies or vaccines that target how immune cells generate energy.

Self-Organizing Agent Teams for Long-Running Scientific Experimentation

AutoScientists changes the game by creating a decentralized “team” of AI agents. Rather than relying on a central planner, these digital scientists look at the shared data and self-organize into specialized groups around the most exciting hypotheses. Before they spend valuable computer processing power on an experiment, they ruthlessly critique each other’s proposals. Crucially, they keep a collective log of both their successes and failures, ensuring the entire system avoids redundant work.

Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision, often requiring researchers to explore multiple competing directions as evidence accumulates and priorities shift. LLM agents can automate parts of this process, but existing agents either concentrate reasoning within a single research thread or coordinate through a central planner with fixed objectives. As a result, they struggle to sustain parallel exploration across research directions or reorganize as promising and unproductive directions emerge over time.

We introduce AutoScientists, a decentralized team of AI agents for long-running computational scientific experimentation. Rather than following decisions from a central orchestrator, agents independently interpret a shared experimental state, self-organize into teams around research directions, critique and filter proposals with a discussion phase before committing experimental compute, and exchange both successful and failed findings across teams to avoid redundant exploration.

Under matched experimental budgets, AutoScientists outperforms prior agentic systems across biomedical machine learning, language-model training optimization, and protein fitness prediction. On BioML-Bench, spanning biomedical imaging, protein engineering, single-cell omics, and drug discovery, AutoScientists achieves a mean leaderboard percentile of 74.4% across 24 tasks, improving over the strongest prior biomedical agent by +8.33%. On GPT training optimization, AutoScientists reaches a target validation bits-per-byte 1.9× faster than autoresearch and continues discovering improvements from a stronger starting champion where the single-agent approach finds none (7 vs. 0 accepted improvements). On ProteinGym fitness prediction, AutoScientists discovers a method for ACE2–Spike binding that improves over the current state-of-the-art model by +12.5% Spearman correlation. Applied without modification to all 217 ProteinGym assays, the same method improves over the prior state of the art by +6.5% in Spearman correlation.

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