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Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity

In addition to suffering seizures, many people with epilepsy also experience bursts of abnormal brain activity called interictal epileptiform discharges (IEDs). These can happen thousands of times a day and interfere with attention, memory, language, and sleep. New data from a study led by scientists at University of California, San Francisco (UCSF) shows that these brain blips are not random events as once thought. The data shows that they unfold in a predictable pattern that can be detected before they occur, suggesting it may be possible to prevent them. 

Details of their work are published in Nature Neuroscience in a paper titled “Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.” In it, the scientists explain that they used a high-resolution technology recently adapted for humans that records individual neuron activity to track more than 1000 neurons in four patients undergoing surgery for epilepsy. The so-called Neuropixel probes provide “a view into new ways we might address a debilitating aspect of epilepsy that we haven’t been able to tackle,” said Jon Kleen, MD, PhD, an associate professor of neurology at UCSF and co-senior author of the study. 

Preventing brain blips would be a boon for patients’ quality of life because over time, the effects of these mental disruptions can be significant and may account for some of the cognitive impairment experienced by about half of people with epilepsy. 

Neuropixels probes, which are thin devices lined with hundreds of sensors, are designed to record activity throughout the human cortex. This means that unlike current sensors which are limited to brain signals on the surface of the brain, Neuropixels can provide a three-dimensional view of brain activity. For the study, the scientists implanted the probes seven millimeters deep into the part of the brain where patients’ seizures originate—this is the tissue that surgeons typically remove to reduce epilepsy symptoms. 

Inserting the probes here made it possible to observe what happened in the neurons before, during, and after each IED. While seizures appear as a burst of neurons firing in synchrony, when IEDs occur, they unfold sequentially. Specifically, one set of neurons was active about a second before the IED started followed by another set that generated the sharp electrical spike at its peak, and then a third set became active as the IED faded. “We could see individual neurons that were just microns apart from each other playing different roles in the process,” said Alex Silva, the study’s first author and a medical student and doctoral candidate in the UCSF-UC Berkeley Joint PhD program in bioengineering. “It was really striking.”

Previous studies have demonstrated that most neurons involved in IEDs are used in normal cognitive processing. According to this study, nearly 80% of the neurons involved in IEDs were also involved in language and perception. Current implantable devices for epilepsy may be able to help. They include closed loop neurostimulators that can detect abnormal brain activity and deliver electrical pulses that interrupt it. So in the case of IEDs, devices that monitor single neurons could use the activity of the first set of neurons announcing the arrival of the abnormal pattern as a warning signal. “That would be a major step forward, changing treatment from reactively responding to abnormal brain bursts to proactively preventing them in the first place,” Kleen said.

The post Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity appeared first on GEN – Genetic Engineering and Biotechnology News.

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CAR T-Cell Therapy Failure Linked to Senescent CD8+ T Cells

Researchers at Rutgers University have identified a factor that may help explain why chimeric antigen receptor (CAR) T-cell therapy fails in a majority cancer patients. In a study published in Cell Reports, the investigators found that the poor initial quality of a patient’s harvested CD8+ T cells that are used to manufacture CAR T-cells lack the ability to mount a robust immune response.

“Many of their T-cells are in a defective state called senescence, which means they can’t proliferate in the lab, they can’t migrate to tissue effectively, and they can’t kill very well,” said senor author Ricardo Iván Martínez-Zamudio, PhD, an assistant professor at Rutgers Robert Wood Johnson Medical School.

Building a CAR T-cell therapy depends on successfully harvesting a patient’s own T cells, then modifying to target tumor cells, growing a robust population of these engineered cells in the lab, then reinfusing them into the patient. But, as the new research shows, the efficacy of this process depends on the inherent capacity of the harvested cells to both proliferate in the lab and to retain their immune function.

The Rutgers study showed that in patients where CAR T therapy is ineffective, a large proportion of a patient’s harvested cells are senescent. Their research demonstrated that CD8+ T cells from donors with higher levels of senescence expanded less under standard CAR T culture conditions than cells from donors with lower senescence levels.

Further, a retrospective look at clinical outcomes of published datasets from lymphoma patients treated with CAR T-cell therapy found that patients whose starting cells and final CAR T-cell products had strong senescence signatures were more likely to fail treatment, while those with lower senescent profiles were more likely to respond. This indicated that the state of CD8+ T cells prior to engineering could be influencing the efficacy of CAR T treatments.

To better understand the molecular basis of CD8+ T cell senescence, the researchers collected blood from both younger and older donors, isolated CD8+ T cells, and used a fluorescent marker to identify senescent cells. They then performed multi-omics profiling, including gene expression and chromatin analysis, to map the regulatory networks controlling senescence.

The resulting data showed that T cell senescence, rather than chronological age of the donor, drives most of the molecular differences in CD8+ T cells. “The senescence program is essentially precoded,” Martínez-Zamudio said. “It’s not that older people develop some new dysfunctional program. The capacity is there from the beginning.”

The study identified a number of transcription factors, including AP1, KLF5, and RUNX2, that regulate this dysfunctional program. When the research altered these to effect gene expression patterns in senescent cells, they were able to partially restore aspects of T cell responsiveness. Their ability to proliferate, however, remained limited.

The implications of this research extend beyond cancer therapy. While it is known that senescent CD8+ T cells accumulate with age and contribute to declines in immune function and chronic inflammation, the study also found that senescence gene signatures were enriched in patients with lupus, suggesting this may also play a role to autoimmune diseases.

“Our study defines the gene-regulatory mechanisms underlying human CD8+ T cell senescence, highlights [transcription factor] network perturbation as a viable strategy to manipulate the senescence state, and identifies senescent CD8+ T cell gene signatures as prognostic tools for immunotherapy outcome,” the researchers wrote.

Based on this, the investigators think that T cell senescence profiling could be used to help determine which patients would benefit from CAR T therapy and those that wouldn’t and could help guide alternative treatments. Because the current findings were a retrospective analysis of patient data, the Rutgers team now plan to test this approach in prospective clinical studies through collaborations with Rutgers Cancer Institute.

The study also indicates the potential to improve CAR T-cell therapy by target the senescence program, by altering transcription factor activity to modify gene expression. But restoring the proliferative capability of these cells using this approach will require more research. Another route for improvement suggested by the research is to develop method to reprogram, or selectively eliminate, senescent cells during the CAR T-cell manufacturing process.

The post CAR T-Cell Therapy Failure Linked to Senescent CD8+ T Cells appeared first on Inside Precision Medicine.

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Evaluating Biomedical Feature Fusion on Machine Learning’s Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Background: Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for the health care system to optimize workflow. Current techniques lack the ability to accurately classify COVID-19 clinical types in patients, especially as SARS-CoV-2 continues to mutate. Objective: We explore the predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and SARS-CoV-2 variants. Methods: Comprehensive patient-level data were collected from 362 patients (severe COVID-19: n=148; nonsevere COVID-19: n=214) infected with the original SARS-CoV-2 strain in 2020 and 1000 patients (severe COVID-19: n=500; nonsevere COVID-19: n=500) infected with the Omicron variant in 2022‐2023. The data included 26 biochemical features from blood testing and 26 clinical features from patients’ clinical characteristics and medical history. Different ML techniques, including penalized logistic regression, random forest, -nearest neighbors, and support vector machines, were applied to build predictive classification models based on each data modality separately and together for each variant. Fifty randomized train-test splits were conducted per scenario, and performance results were recorded. Results: The fusion (hybrid) characteristic modality yielded the highest mean area under the curve (AUC) in this study, achieving 0.915, while the biochemical and clinical modalities had AUCs of 0.862 and 0.818, respectively. All ML models performed similarly under different testing scenarios and were consistent when cross-tested with data of patients infected with the original strain and those infected with the Omicron variant. Our models ranked elevated d-dimer (biochemical), elevated high sensitivity troponin I (biochemical), and age greater than 55 years (clinical) as the most positively predictive features of severe COVID-19. Conclusions: These results are compatible with the hypothesis that ML is a useful tool for predicting severe COVID-19 based on comprehensive individual patient–level data. Further, ML models trained on the biochemical and clinical modalities together show patterns consistent with enhanced predictive performance. The improved performance observed with Omicron variant data agrees with the hypothesis that ML approaches may retain utility across variants in this study setting, although further validation is required before clinical application. Future work using larger datasets with more ethnic variation and investigating unbiased ML interpretation methods may be able to provide further validation.
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Use of a Large Language Model to Reveal Narrative Architectures of Veteran Transition Stress: Development and Validation Study

Background: The stress caused by multiple aspects of veterans’ transitions from military to civilian, termed transition stress, represents a unique source of psychological impact that is underresearched due to its qualitative nature. The assessment of this complex psychological phenomena has thus relied on laborious interviews designed to extract quantitative information from qualitative narratives of the transition to civilian life. We sought to determine if large language models (LLMs) could be used as valid measurement tools to extract relevant information from open-ended narratives. Objective: This study sought to develop and validate a generative artificial intelligence (AI) approach to automate the quantification and subsequent thematic analysis of veteran transition stress. Methods: Utilizing transcripts from interviews of a sample of US military veterans, we developed an LLM to rate transition stress severity and examined the model’s reliability in relation to human coders and validity in relation to a set of related questionnaire measures. Next, we used the LLM scores to quantitatively define high and low transition stress groups, enabling a targeted, automated analysis of themes related to narrative identity and life transition themes that might differentiate the two groups. Results: LLM ratings of transition stress correlated highly with the human expert ratings and showed significant, theoretically congruent correlations with measures of clinical symptoms, reintegration difficulties, and veterans’ self-ratings of transition difficulty. Critically, the AI-derived thematic analyses of the narratives from high and low transition stress veterans revealed clearly distinct and informative patterns. Conclusions: These findings suggest that generative AI offers a robust, scalable, and reliable method for multidimensional analysis of complex, narrative-based psychological constructs.

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STAT+: As artificial intelligence shows off diagnostic chops, scientists reckon with the way forward

Getting a paper published in Science is a highlight of many researchers’ careers. But for internist and clinical artificial intelligence researcher Adam Rodman, it’s also been a source of some agita. 

On Thursday, Rodman and his colleagues published a compilation of experiments, including one using real-world data from a Boston emergency department, that show a large language model from OpenAI can outperform physicians in case-based diagnostic and clinical reasoning evaluations. To Rodman, the paper’s co-senior author, it’s a response to a gauntlet thrown down in Science in 1959. That paper “described how you would know that a clinical decision support system was capable of doing diagnosis better than humans,” he said. “And they can do it.”

But as generative AI tools like chatbots are heavily marketed — both to patients and clinicians — it makes him worried that the science experiments, all based on simulated and historical cases, will be misconstrued as proof of AI’s safety and efficacy when used to treat real patients. 

Continue to STAT+ to read the full story…

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Stem Cell Memory CAR T Therapy Proves Effective at Low Doses

Results from a Phase I clinical trial show promise for a CAR T therapy using a population of stem cell memory T cells (TSCM), achieving complete remission at low doses and lowering the risk of serious adverse effects including cytokine-release syndrome. The study was published in Cell today.

“Seeing patients achieve complete responses at doses as low as 250,000 cells per kilogram, without chemotherapy preconditioning, validates years of preclinical work and opens a new chapter in CAR T-cell design,” says Luca Gattinoni, MD, head of the research division for functional immune cell modulation at the Leibniz Institute for Immunotherapy (LIT) and lead author of the study.

While CAR T therapy has significantly improved treatment of blood cancers, many patients still struggle to experience lasting benefits. One potential cause for this is that the infused CAR T cells can often fail to expand and persist within the body over time. To overcome this challenge, Gattinoni’s team treated patients using a TSCM population with strong self-renewal and proliferation capacity. 

“Today’s CAR T-cell products are heterogeneous, and that variability is reflected in the range of clinical responses and toxicity profiles we see in patients,” explains Gabriele Inchingolo, PhD candidate in Gattinoni’s team and a lead author of the study. “To address this, we developed a highly homogeneous CD8+ CAR T-cell product selectively enriched for TSCM cells and compared its performance to conventional CAR T cells.”

The first-in-human trial recruited patients with relapsed or refractory CD19 B-cell malignancies who had previously received a hematopoietic stem cell transplantation (HSCT), a patient population with very limited therapeutic options. Whether they received conventional CAR T cells or a TSCM-enriched product, no patients received chemotherapy preconditioning, which is typically used before infusing CAR T cells to help the cells engraft.

Compared to standard CAR T cells, the CAR TSCM cells showed greater expansion and persistence, allowing them to achieve complete responses even at low doses. 

“We have shown that a more defined, stem-like cell product can perform effectively at lower doses. By employing a highly homogeneous TSCM population, we can potentially achieve more consistent engraftment and persistence, paving the way for more predictable outcomes and more rationally designed clinical trials,” says Gattinoni.

In addition, the TSCM cells showed a more favorable safety profile. Even at expansion levels that caused severe cytokine-release syndrome in patients treated with conventional CAR T cells, patients treated with the TSCM cells only experienced mild side effects.

“We observed less cytokine-release syndrome in this study compared to most other CAR clinical trials that I have participated in,” adds James Kochenderfer, MD, senior investigator at the surgery branch of the National Cancer Institute (NCI). “The TSCM platform yielded higher CAR T-cell levels on a per cell basis—and across many CAR T-cell studies, high blood CAR T-cell levels have been one of the strongest predictors of clinical efficacy.”

While not every patient responded to the CAR TSCM cell therapy, results showed that treatment failure was driven by external factors such as low levels of the target CD19 protein on tumor cells, immunosuppressive signals such as IL-10, and immune responses against the CAR construct. Future studies in larger cohorts will evaluate the addition of chemotherapy preconditioning and CD4 T cells to potentially continue improving patient outcomes, as well as expanding this approach into other forms of cancer, including solid tumors. 

The post Stem Cell Memory CAR T Therapy Proves Effective at Low Doses appeared first on Inside Precision Medicine.

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<![CDATA[A multicenter study tests SAINT TMS for postpartum depression.]]>
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This startup’s new mechanistic interpretability tool lets you debug LLMs

The San Francisco–based startup Goodfire just released a new tool, called Silico, that lets researchers and engineers peer inside an AI model and adjust its parameters—the settings that determine a model’s behavior—during training. This could give model makers more fine-grained control over how this technology is built than was once thought possible.

Goodfire claims Silico is the first off-the-shelf tool of its kind that can help developers debug all stages of the development process, from building a data set to training a model.

The company says its mission is to make building AI models less like alchemy and more like a science. Sure, LLMs like ChatGPT and Gemini can do amazing things. But nobody knows exactly how or why they work, and that can make it hard to fix their flaws or block unwanted behaviors. 

“We saw this widening gap between how well models were understood and just how widely they were being deployed,” Goodfire’s CEO, Eric Ho, tells MIT Technology Review in an exclusive chat ahead of Silico’s release. “I think the dominant feeling in every single major frontier lab today is that you just need more scale, more compute, more data, and then you get AGI [artificial general intelligence] and nothing else matters. And we’re saying no, there’s a better way.”

Goodfire is one of a small handful of companies, including industry leaders Anthropic, OpenAI, and Google DeepMind, pioneering a technique known as mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by mapping its neurons and the pathways between them. (MIT Technology Review picked mechanistic interpretability as one of its 10 Breakthrough Technologies of 2026.)  

Goodfire wants to use this approach not only to audit models—that is, studying those that have already been trained—but to help design them in the first place.  

“We want to remove the trial and error and turn training models into precision engineering,” says Ho. “And that means exposing the knobs and dials so that you can actually use them during the training process.”

Goodfire has already used its techniques and tools to tweak the behaviors of LLMs—for example, reducing the number of hallucinations they produce. With Silico, the company is now packaging up many of those in-house techniques and shipping them as a product.

The tool uses agents to automate much of the complex work. “Agents are now strong enough to do a lot of the interpretability work that we were doing using humans,” says Ho. “That was kind of the gap that needed to be bridged before this was actually a viable platform that customers could use themselves.”

Leonard Bereska, a researcher at the University of Amsterdam who has worked on mechanistic interpretability, thinks Silico looks like a useful tool. But he pushes back on Goodfire’s loftier aspirations. “In reality, they are adding precision to the alchemy,” he says. “Calling it engineering makes it sound more principled than it is.”

Mapping models

Silico lets you zoom in on specific parts of a trained model, such as individual neurons or groups of neurons, and run experiments to see what those neurons do. (Assuming you have access to the model’s inner workings. Most people won’t be able to use Silico to poke around inside ChatGPT or Gemini, but you can use it to look at the parameters inside many open-source models.) You can then check what inputs make different neurons fire, and trace pathways upstream and downstream of a neuron to see how other neurons affect it and how it affects other neurons in turn.

For example, Goodfire found one neuron inside the open-source model Qwen 3 that was associated with the so-called trolley problem. Activating this neuron changed the model’s responses, making it frame its outputs as explicit moral dilemmas. “When this neuron’s active, all sorts of weird things happen,” says Ho.

Pinpointing the source of odd behavior like this is now pretty standard practice. But Goodfire wants to make it easier to adjust that behavior. Using Silico, developers can now adjust the parameters connected to individual neurons to boost or suppress certain behaviors.

In another example, Goodfire researchers asked a model whether a company should disclose that its AI behaves deceptively in 0.3% of cases, affecting 200 million users. The model said no, citing the negative business impact of such a disclosure.

By looking inside the model, the researchers found that boosting neurons that were found to be associated with transparency and disclosure flipped the answer from no to yes nine out of 10 times. “The model already had the ethical reasoning circuitry, but it was being outweighed by the commercial risk assessment,” says Ho.

Tweaking the values of a model in this way is just one approach. Silico can also help steer the training process by filtering out certain training data to avoid setting unwanted values for certain parameters in the first place.   

For example, many models will tell you that 9.11 is greater than 9.9. Looking inside a model to see what’s going on might reveal that it is being influenced by neurons associated with the Bible, in which verse 9.9 comes before 9.11, or by code repositories where consecutive updates are numbered 9.9, 9.10, 9.11 and so on. Using this information, the model can be retrained to make it avoid its “Bible” neurons when doing math.

By releasing Silico, Goodfire wants to put techniques previously available to a few top labs into the hands of smaller firms and research teams that want to build their own model or adapt an open-source one. The tool will be available for a fee determined on a case-by-case basis according to customers’ requirements (Goodfire declined to give specific pricing details).

“If we can make training models a lot more like building software, there’s no reason why there can’t be many more companies designing models that fit their needs,” says Ho.

Bereska agrees that tools like Silico could help firms build more trustworthy models. These techniques could be essential for safety-critical applications in health care and finance, he says.

“Frontier labs already have internal interpretability teams,” he adds. “Silico arms the next tier of companies, where the value is not having to hire interpretability researchers.”

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Pathogenic Bacterium Rewires Gut Environment to Colonize and Cause Disease

An international research team headed by scientists at Vanderbilt University Medical Center has shown how an intestinal pathogen reshapes the gut environment to fuel its own colonization and cause disease. The team’s studies found that enterotoxigenic Bacteroides fragilis (ETBF) uses a toxin it produces, Bacteroides fragilis toxin (BTF), to reprogram intestinal cell metabolism and generate conditions that support its growth. ETBF is a classically anaerobic bacterium that causes diarrhea and has been implicated in inflammatory diseases, including colitis and colorectal cancer. The study findings point to potential new therapeutic strategies for disrupting the growth of pathogens such as ETBF.

“Our findings suggest that disease-associated microbes don’t just respond to inflammation—they can actively drive it by reshaping host metabolism,” stated Wenhan Zhu, PhD, assistant professor of pathology, microbiology and immunology. “This opens up new possibilities for intervention, such as by targeting metabolic interactions between host and microbes to prevent or disrupt diseases like infectious diarrhea and colorectal cancer.

Zhu is lead corresponding author of the team’s published paper in Cell, titled “An anaerobic pathogen rewires host metabolism to fuel oxidative growth in the inflamed gut.” In their paper the team wrote, “Here, we demonstrate that ETBF leverages its virulence factor, BFT, to reprogram epithelial cell metabolism, thereby reshaping the gut nutritional landscape. This reprogramming leads to increased levels of lactate and oxygen, which fuel ETBF’s unique oxidative metabolism.”

Independent studies have implicated ETBF in both inflammatory diarrheal diseases and in colorectal cancer, the authors noted. “These pathogenic effects are primarily driven by the virulence factor Bacteroides fragilis toxin (BFT), which elicits a range of physiological alterations in host cells.” However, the team noted, “… the specific mechanisms by which BFT facilitates ETBF niche establishment and promotes persistent colonization in the gut remain largely undefined.”

Zhu has long been interested in how pathogens succeed in the competitive intestinal environment. “The gut is one of the most densely populated microbial environments in the body, with heavy competition for nutrients, yet certain microbes can still take hold and drive disease,” he said. “These microbes are ultimately competing for nutrients, and processes like inflammation and cancer may be ways they alter the environment to gain access to those resources.”

Though the percentage of people who carry ETBF varies from study to study, it can be a common member of the gut microbiota and is considered a classical anaerobe, a type of bacteria that requires low-oxygen conditions (such as those in the large intestine) to survive. It produces a toxin, BFT, that interacts with intestinal host cells, causing inflammation and increasing oxygen and oxidative stress—conditions that are usually harmful to anaerobes such as ETBF.

Zhu and colleagues are exploring how ETBF navigates and exploits these conditions, to gain insight into microbial physiology and host-microbe interactions, he said. Through their newly reported study the investigators found that ETBF uses its toxin, BFT, to reprogram intestinal epithelial cell metabolism.

The researchers discovered that ETBF reshapes the intestinal landscape in unexpected ways, for example by driving epithelial cell proliferation and manipulating immune signaling pathways and bile acid biology. “BFT manipulates colonic epithelial signaling and the bile acid recycling pathway, inducing a metabolic shift in the epithelium from oxidative phosphorylation to glycolysis,” they wrote.

This metabolic shift reduces oxygen consumption by host cells, increasing oxygen availability in the gut. The resulting environment supports the growth of ETBF, despite it being traditionally considered an anaerobe. “This shift increases local concentrations of lactate and oxygen, nutrients that support oxidative metabolism in ETBF,” they continued. These changes also create conditions that promote disease-associated microbial communities linked to colorectal cancer.

“One of our most surprising findings was that a classically anaerobic bacterium can benefit from, and even help create, an oxygen-rich environment,” Zhu said. “This challenges the traditional view that anaerobic microbes simply cannot tolerate oxygen.”

The team is continuing to explore how ETBF modifies its environment to successfully colonize and cause disease; how broadly the mechanisms apply across other microbes and disease settings; and whether these interactions can be therapeutically targeted. In their report the investigators stated, “… by sculpting an oxidative niche, ETBF both fuels its own growth and suppresses its microbial competitors. Importantly, this distinct metabolic program could potentially be leveraged to selectively target and remove ETBF.” Zhu added, “Ultimately, we hope to identify strategies to disrupt these disease-promoting niches before they lead to long-term pathology.”

The post Pathogenic Bacterium Rewires Gut Environment to Colonize and Cause Disease appeared first on GEN – Genetic Engineering and Biotechnology News.

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Long-Term SHIV Suppression Using AAV Gene Therapy

While the overwhelming scope of tragic outcomes from HIV infection at the origin of the AIDS epidemic are in the past, those living with HIV still require daily treatments. One option includes combination antiretroviral therapy (cART) which can suppress viral replication to undetectable levels. While this therapy is effective, HIV-infected CD4+ T cells still remain in the body and inconsistent adherence to therapy schedules can result in increased viral replication to detectable levels, possibly also causing symptoms.

Treatment with C-C chemokine receptor 5 (CCR5)-specific antibodies are one of a few alternative therapies for HIV infection, however dosing strategies and maintenance is challenging for both patients and manufacturers.

Researchers at Oregon Health & Science University Oregon National Primate Research Center aimed to address the need for long-term expression of CCR5-specific antibodies to establish protection from HIV using adeno-associated virus (AAV) vectors.

Their work was published in Science Translational Medicine under the title, “Adeno-associated virus gene therapy-mediated CCR5 blockade suppresses virus replication long-term in SHIV-infected macaques.”

“We explored the ability of AAV vectors expressing the CCR5-blocking antibody leronlimab to mediate a functional cure in simian-human immunodeficiency virus (SHIV)–infected rhesus macaques by interrupting viral access to the viral entry co-receptor CCR5,” wrote the authors.

Leronlimab is an antiviral HIV drug that targets and blocks the CCR5 receptor, thus blocking HIV’s ability to invade immune cells. Nineteen SHIV-infected macaques were treated with leronlimab expressing AAVs. All but one treated macaque produced detectable levels of leronlimab following AAV administration. The single animal that didn’t produce leronlimab had preexisting leronlimab-specific antidrug antibodies (ADA).

About half of the animals developed an immune response to the therapy, producing ADA clearing of leronlimab, however, over a year of observation, researchers found latent increase in stable expression of the leronlimab. Macaques that did not exhibit an immune response maintained leronlimab expression throughout the same year of observation.

Most macaques that produced sufficient number of antibodies showed long-term partial or full suppression of SHIV. “Of the nine macaques producing sufficient leronlimab to achieve full CCR5 receptor occupancy on blood CD4+ T cells, AAV-leronlimab drove stringent or partial control of SHIV viremia in six macaques long term,” wrote the authors. The three remaining macaques, when given an additional dose of leronlimab, showed either complete viral suppression or 100-fold reduction in viral load.

The authors explain that these results indicate that there is a “threshold of leronlimab expression [that] is necessary to effectively halt SHIV replication.” They also point out that while they tested multiple capsids and promotors, they were limited in assessing vector design or dose, but surmise that the AAV-leronlimab could be combined with other AAV-delivered antivirals for a multitargeted approach.

“These results demonstrate the potential of gene therapy–mediated long-term antibody-based CCR5 blockade for HIV functional cure but highlight challenges in achieving sufficient antibody expression when targeting an abundant self-antigen,” concluded the authors.

The post Long-Term SHIV Suppression Using AAV Gene Therapy appeared first on GEN – Genetic Engineering and Biotechnology News.