Tag: Biotech

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Big Tech Targets Drug Discovery with Wave of Life Science Platforms

Nvidia CEO, Jensen Huang, asserts that accelerated computing has a missing word: application acceleration. The “vertically integrated” and “horizontally open” chip maker is set on building the infrastructure that delivers AI into real world use. 

“Accelerated computing is not a chip problem,” said Huang when he took the stage for his annual NVIDIA GTC keynote in San Jose in March. “The only way for us to accelerate applications and bring tremendous speed up and cost reduction is through domain specific acceleration.”  

That mission has hit drug discovery, where approval timelines exceed a decade and clinical trial failure rates approach 90%.  

A new wave of platforms from Amazon Web Services (AWS), OpenAI, and Anthropic have customized general-purpose assistants into AI-powered workflows for science research. The trend points to the growing role of cloud infrastructure and agentic AI in unifying fragmented tools, streamlining data management, and making domain expertise more accessible. 

Lab-in-the-loop 

In April, AWS introduced Amazon Bio Discovery, a “lab-in-the-loop” workflow that combines access to more than 40 open-source and proprietary biological foundation models with AI agents that guide experimental design. The platform also integrates CRO partners, including Twist Bioscience, Ginkgo Bioworks, and A-Alpha Bio, for lab validation. The launch was announced at the AWS Life Sciences Symposium at the Javits Center in New York. 

In collaboration with Memorial Sloan Kettering Cancer Center, Amazon Bio Discovery designed nanobodies with nanomolar affinities by generating nearly 300,000 candidates that were narrowed to the top 100,000 for wet lab testing in weeks, a noticeable reduction from the up to one year timeline typical of traditional methods. 

Dan Sheeran, vice president and general manager, healthcare and life science at AWS, explains that while biological AI models have driven breakthroughs in areas, like protein design, their reliance on coding expertise and complex compute infrastructure remains a significant barrier to broader accessibility. 

“Choosing the right model for a given task is itself a significant challenge. Computational biologists, the specialists who bridge AI and biology, are in short supply,” Sheeran told GEN Edge. “The result is a collaboration bottleneck, not because the science isn’t available, but because the tooling doesn’t support how these teams need to work together.” 

David Younger, PhD, co-founder and CEO of A-Alpha Bio, adds that the partnership with AWS highlights a “fundamental gap” in AI-powered drug discovery, the lack of high-quality, experimental data at scale to evaluate protein design models. In silico candidates designed using Amazon Bio Discovery can be rapidly validated in the lab with A-Alpha’s AlphaSeq platform, which quantitatively measures protein-protein interactions by the hundreds to millions. 

“The convergence of technology and life sciences isn’t just about faster compute or better algorithms,” Younger told GEN Edge. “It’s about connecting those advances to real-world, experimental observations.” 

Amazon Bio Discovery is built on the same AWS infrastructure that is currently adopted by 19 of the top 20 global pharmaceutical companies. Each organization’s data is isolated within its application environment, and all proprietary data, models, and designs remain customer-owned. 

Rosalind reasons 

Two days after Amazon Bio Discovery’s launch, OpenAI announced GPT-Rosalind, a specialized reasoning model that supports evidence synthesis, hypothesis generation, and experimental planning for research across biology, drug discovery, and translational medicine. The platform includes a freely accessible life sciences research plugin for Codex that connects to over 50 public multiomics databases, literature repositories, and computational biology tools.  

The model is available through a trusted-access program for qualified enterprise customers in the U.S. Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific are among GPT‑Rosalind’s customers. 

“Research organizations are actively looking for systems that are built for scientific workflows, not adapted from general-purpose models, and life sciences remains one of the most important areas where better tools could meaningfully accelerate progress,” wrote OpenAI in an email to GEN Edge when describing the motivation for building GPT-Rosalind. 

Named after Rosalind Franklin, PhD, whose work was critical in the discovery of the DNA double helix, the model scored 0.751 on BixBench, a benchmark that evaluates large language model (LLM) performance in bioinformatics and computational biology tasks. The score was a modest lead ahead of GPT-5.4, xAI’s Grok 4.2, and Google’s Gemini 3.1 Pro. 

On LABBench2, a benchmark spanning literature retrieval, database access, sequence manipulation, and protocol design, GPT-Rosalind outperformed GPT-5.4 on six out of 11 tasks. The largest improvement was shown on CloningQA, which requires end-to-end design of DNA constructs and enzyme reagents for molecular cloning workflows. 

GPT-Rosalind is one step in OpenAI’s growing momentum across pharma and healthcare. In recent weeks, the company introduced ChatGPT for Clinicians to support clinical workflows, such as documentation and medical research, alongside partnerships with Novo Nordisk to enhance workforce AI readiness and improve manufacturing and supply chain efficiency, and Massive Bio to expand access to clinical trials. 

Inference inflection 

Anthropic is forging its own path into life sciences, having recently drawn attention for acquiring Coefficient Bio, a roughly 10-person AI drug discovery start-up founded by former Genentech scientists, for $400 million.  

The OpenAI competitor has also been building Claude for Life Sciences, the AI assistant specialized for researchers, clinical coordinators, and regulatory affairs managers, since last fall. 

In an October blog post, Anthropic reported that the customized platform powered by Claude Sonnet 4.5 scored 0.83 in Protocol QA, a benchmark that tests the model’s understanding of laboratory protocols. The score outperformed the human baseline of 0.79 and Sonnet 4’s performance of 0.74. Claude for Life Sciences also incorporates several connectors to scientific platforms, including Benchling’s digital notebooks, PubMed literature, and 10x Genomics tools for single cell and spatial analysis.  

“We want to give scientists the same experience as software engineers of having a brainstorming partner to work with and to delegate tasks,” said Eric Kauderer-Abrams, PhD, head of biology and life sciences at Anthropic, in a video accompanying the product launch. 

In January, Anthropic expanded the platform to Claude for Healthcare, a complementary set of tools that allow healthcare providers, payers, and health tech companies and startups to use Claude for medical purposes through HIPAA-ready products.

When reflecting on these life science releases, Enke Bashllari, PhD, founder and managing director at Arkitekt Ventures, says the three are “playing different games.” OpenAI is selling the “sharpest reasoning engine” with limited access, while AWS is building infrastructure and lab integration. Anthropic is betting on breadth of workflow and making acquisitions to close the specialization gap.  

“For startups, the question isn’t which platform wins. It’s which layer you build on,” wrote Bashllari on LinkedIn. 

Chris Leiter, founder and general partner at Atria Ventures, believes the shift to bioconsumerism will be the “most significant period of disruption for life sciences in the modern era.” 

“Medicine is the use case that justifies the entire buildout,” wrote Leiter on LinkedIn. “The public skepticism starts to erode when the output is a drug that reaches a patient five years early, or a diagnostic that catches a cancer no doctor would have seen.” 

As models increasingly move beyond isolated predictions into complex reasoning across biological systems, the question is no longer whether to adopt, but how quickly the industry can adapt to a new scientific discovery paradigm. 

Huang says it best, “we are now in the beginning of a new platform shift. The inference inflection has arrived.” 

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STAT+: Biotech raises $42 million to run Huntington’s disease trial

Gene therapy startup Latus Bio has raised another $42 million to start its first clinical trials, where it will try to sidestep issues that have set back a more advanced competitor. 

Latus is moving two treatments through clinical trials this year. The first is for a form of Batten disease called CLN2 disease, a fatal genetic condition that causes seizures, vision loss, and cognitive problems. The company anticipates having initial clinical data by the end of the year. 

Now, Latus — founded by Beverly Davidson, chief scientific strategy officer at the Children’s Hospital of Philadelphia — is turning its attention to a second drug candidate, a gene therapy for Huntington’s disease.

Continue to STAT+ to read the full story…

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Opinion: STAT+: China’s strict new supply chain regulations could create massive problems for Western biopharma companies

A recently published paper delivers a finding that should be well known to every executive in Western pharma: China has gone from accounting for less than 8% of global clinical trials in 2010 to surpassing the United States in annual registered trial volume by 2020, reaching more than 5,000 trials per year in 2024. Eighty-eight percent of that growth, among private-sector sponsors, was driven by domestic Chinese firms, not multinationals relocating R&D.

China is not just a manufacturing base for the West’s drug supply chain. It is now a peer competitor in drug innovation — and it intends to stay that way on its own terms.

Toward that end, on April 7, China’s State Council issued Decree No. 834, the Regulations on Industrial and Supply Chain Security, effective immediately, with no transition period. Its 18 articles give Beijing sweeping new powers to investigate and sanction any foreign company whose commercial decisions are deemed to harm China’s industrial chain security. China’s 15th five-year plan has explicitly designated biotechnology and pharmaceuticals as the centerpiece of its next phase of industrial development. Decree No. 834 is the legal infrastructure through which Beijing intends to protect and leverage that ambition.

Continue to STAT+ to read the full story…

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Medtech supplier Promex names new president

Promex Industries has promoted Chief Operating Officer Dave Fromm to president and COO of the medtech supplier and its QP Technologies division effective immediately. Fromm will oversee strategic direction, operational execution and the the packaging and assembly company’s  growth. Promex Industries and QP Technologies CEO Dick Otte formerly served as president. “After more than 30…

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Opinion: STAT readers on MAHA activists, perimenopause, and diversity in medical school

First Opinion is STAT’s platform for interesting, illuminating, and provocative articles about the life sciences writ large, written by biotech insiders, health care workers, researchers, and others.

To encourage robust, good-faith discussion about issues raised in First Opinion essays, STAT publishes selected Letters to the Editor received in response to them. You can submit a Letter to the Editor here, or find the submission form at the end of any First Opinion essay.

Read the rest…

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Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models

In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own. 

The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse. 

“I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said.

Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.

This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor. 

Who is the steward of AI safety?

During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.” 

“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury.

Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination. 

Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record. 

The lawyers then entered a heated debate about who was the true guardian of AI safety. 

The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely.

“Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.”

When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said. 

When did Musk think he was being duped?

As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out. 

Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.” 

In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.)

“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” he told the jury, “as long as the tail didn’t wag the dog.” 

But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI. 

“I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said.

Is Musk just trying to kill competition?

But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023.

Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote.

When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.

Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.” 

Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company.

But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury. 

In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped. 

Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products. 

“It is standard practice to use other AIs to validate your AI,” argued Musk.

Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify.

This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

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DNA-Containing Extracellular Vesicles Boost Antitumor Responses in Mice

A study led by investigators at Weill Cornell Medicine has found that activated T cells secrete extracellular vesicles (EVs) containing DNA, which can enter other immune and tumor cells to stimulate the body’s defense systems. Preclinical experiments showed that this vesicle-associated DNA could be useful therapeutically, boosting T cell attacks against tumors that otherwise evoke little or no immune response.

Studies in live mice showed that these activated T cell-derived-EVs (AT-EVs) enhanced antigen processing and presentation (APP) in tumor cells and dendritic cells (DCs) across different immunologically cold tumors. The ATEVs also synergized with immune checkpoint inhibitors (ICIs) to trigger antitumor immunity and hold back tumor growth.

The discovery extends the scientific understanding of the immune system, identifies a new strategy for boosting immunity against cancers, and potentially offers a new tool for delivering genetic payloads to other cells. “These findings reveal a natural mechanism for treating immunologically silent tumors and other diseases that stem from insufficient immune surveillance,” said David Lyden, MD, PhD, the Stavros S. Niarchos professor in pediatric cardiology and a member of the Gale and Ira Drukier Institute for Children’s Health and the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine.

Lyden is co-senior author of the researchers’ published paper in Cancer Cell, titled “Activated T cell extracellular vesicle DNA transfer enhances antigen presentation and anti-tumor immunity,” in which they stated, “We uncover a mechanism whereby activated T cell-derived extracellular vesicles (ATEVs) drive a positive feedback loop that enhances antigen presentation and immune responses in normal physiology and cancer … Notably, ATEVs hold promise as an acellular immunotherapy, restoring APP and synergizing with checkpoint blockade in immunotherapy-refractory tumors.”

Most animal cells secrete extracellular vesicles which can contain cargo including proteins, snippets of DNA, and other molecules. “Extracellular vesicles (EVs) are nanoparticles naturally released by all living cells, containing proteins, lipids, and genetic material, that facilitate intercellular communication,” the investigators wrote.

The Lyden lab in recent years has made seminal discoveries about extracellular vesicles and their functions, finding for example that vesicles secreted by tumor cells can influence the immune system’s anti-tumor response. Their findings, they noted, “… raised the possibility that EVDNA from immune cells, such as T cells, may also have immune-related functions.” For their new study the team examined the roles of vesicles secreted by immune cells, and specifically T cells, which are the immune system’s principal tumor-fighters.

In their initial experiments, the scientists found that under physiological conditions, T cell-secreted vesicles tend to home to lymph nodes, spleen and other centers of immune activity. There the vesicles are preferentially taken up by antigen-presenting immune cells, including dendritic cells, which assist in T cell activation, a critical process in the immune response. The researchers found that the overall effect of these vesicles released by activated T cells is to boost the antigen-presenting process, thus promoting T cell priming and broader immune activation. The key payloads in these immune-boosting vesicles turned out to be snippets of T cell DNA.

“These surprisingly abundant DNA fragments are mostly on the surfaces of the vesicles, and are not just random—they are enriched for immune-related genes, including genes that help cells display antigens to the immune system,” said co-senior author Haiying Zhang, PhD, an assistant professor of cell and developmental biology in pediatrics and member of the Lyden lab. “We also found that these vesicles have, attached to their surfaces, a special enzyme that acts as a molecular drill, enabling the transfer of vesicle-carried DNA into the nucleus of the recipient cell where they can be expressed transiently,” added study co-first author Diao Liu, PhD, a postdoctoral research associate in the Lyden Lab.

Infusing DNA-carrying vesicles from activated T cells into mice with tumors, the researchers found that the vesicles were taken up not only by antigen-presenting cells but also by tumor cells themselves. The treated tumors grew more slowly and were better infiltrated by T cells and other immune cells, indicating that the vesicles induced a stronger anti-tumor response. “Our work reveals an EV-mediated mechanism through which activated T cells enhance APP across diverse recipient cells—from DCs in physiological conditions to cancer cells across tumor types,” the authors noted. Although cancers—and viruses—frequently suppress the antigen-presenting process to make malignant or infected cells “invisible” to the immune system, the main effect of the extracellular vesicular DNA was to reverse this process, restoring tumor cells’ visibility.

The team demonstrated the effectiveness of this approach, alone and in combination with existing immunotherapy, in preclinical models of three different immunologically silent cancers: glioblastoma, pancreatic and triple-negative breast cancer. “By boosting APP machinery, ATEVs enhance tumor immunogenicity and elicit robust anti-tumor responses, particularly when combined with ICIs in otherwise resistant tumors, including pancreatic, breast, and brain cancers,” they stated. “These findings reveal the translational potential of activated T cell-derived extracellular vesicles (ATEVs) by exploiting a naturally occurring immune-boosting process to overcome immune evasion, particularly in immunologically silent cancers.”

Co-senior author Irina Matei, PhD, an assistant professor of immunology research in pediatrics and member of the Lyden lab, stated, “There seems to be a positive-feedback loop, in which the DNA-carrying vesicles from activated T cells amplify the immune response by acting on both antigen-presenting cells, which increase expression of the machinery processing tumor antigens, and tumor cells, promoting their recognition by the immune system as well as their own production of DNA-laden vesicles.”

The researchers are now working to translate their findings into a new, vesicle-based cancer treatment, which could be used on its own or in conjunction with standard immunotherapies or other cancer treatments. “The surprising ability of these vesicles to transfer DNA from donor T cells into the nuclei of recipient cells suggests their potential as a natural, non-viral platform for transient gene delivery,” said co-first author Mengying Hu, PhD, a postdoctoral research associate in the Lyden Lab who led the research and is now an assistant professor of pharmaceutical sciences at the Ohio State University. “The results point to a broadly applicable gene-transfer strategy that may offer improved safety and efficiency compared with current gene therapy approaches.”

In their paper the authors concluded, “Overall, ATEVs emerge as an acellular immunotherapy and delivery modality that can prime antitumor immunity, synergize with existing therapies, and serve as a vaccine adjuvant,” they concluded. “Our findings provide a foundation for the therapeutic application of ATEVs through a deeper understanding of the biological role of AT-EVDNA.”

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Restoring Protein Recycling Reverses T-Cell Exhaustion in Mice

New research published by scientists at the University of California, San Diego (UCSD), describes an unexpected factor underlying T-cell exhaustion. The details of their work in mice are published in a new Cell paper titled “Proteostasis sustains T-cell differentiation potential and tumor-infiltrating lymphocyte function.”

T cells are critical members of the immune system but there are limits to their defensive capabilities. When fighting cancer cells, T cells often burn out and become dysfunctional. A major focus of current cancer immunotherapy efforts is rescuing T cells from this state and getting them back into cancer-fighting shape. The new Cell study led by scientists in the lab of Ananda Goldrath, PhD, a professor of molecular biology at UCSD, and their collaborators elsewhere, suggests that a potential solution to T-cell exhaustion might have to do with protein recycling.

Specifically, their finding has to do with proteostasis, the network of cellular processes that orchestrates the proper construction, movement, and destruction of proteins in cells. A component of this network features a type of recycling function where healthy cells continuously dismantle old and damaged proteins to preserve energy and reuse building blocks to make new proteins. According to the paper, the scientists uncovered an impaired protein recycling function as the surprise culprit in T-cell exhaustion. 

“We found that exhausted T cells’ recycling programs are falling apart, leading to damaged and misfolded proteins that pile up with nowhere to go,” said Nicole Scharping, PhD, a post-doctoral fellow in the Goldrath lab and lead author on the paper. Additionally, the scientists also uncovered a way to reverse the accumulation of misfolded proteins by fixing the broken recycling function and restoring normal proteostasis. As Scharping explained, the issue can be resolved with a “tag and sort” fix. This is accomplished using E3 ligase enzymes which act as labelers at a recycling facility, tagging worn-out proteins so the cell knows to break them down.

“In exhausted T cells, many of these enzymes get switched off, and recycling grinds to a halt,” said Scharping. After examining thousands of proteins, the scientists honed in on NEURL3, RNF149 and WSB1 as the E3 ligases responsible for rescuing T cell recycling functions. “When we restored specific E3 ligases, the buildup cleared, and the T cells regained their function and worked better at clearing tumors.” While the new study was conducted in mice, the researchers indicate that similar strategies could be employed for immunotherapy treatments in human cancer.

Importantly, the findings may have implications in other diseases as impaired protein processing is not unique to exhausted T cells. “We think this loss of proteostasis resembles what occurs in neurons in other protein aggregate diseases such as Parkinson’s and Alzheimer’s,” said Goldrath. “Rescuing these cells from exhaustion could improve the ability of T cells to respond to both chronic infection as well as tumors.”

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Figurate SCADA System Launched to Overcome Digital Bottlenecks During Biopharma Manufacturing

Cytiva and Rockwell Automation launched the figurate supervisory control and data acquisition (SCADA) system designed to remove digital bottlenecks during biopharmaceutical manufacturing. Working across multiple instrument vendors and modalities, Figurate SCADA provides the connectivity needed to enable digital integration to advance modern bioprocessing, according to Matt Weaver, vice president of global industry life sciences at Rockwell.

“Biopharma teams are under pressure to move more quickly, but their systems are often not built to keep up,” says Weaver. “This collaboration with Cytiva marks a pivotal step in our mission to democratize digital manufacturing, enabling biopharma innovators to deploy SCADA faster, smarter and more affordably.”

Many biopharma teams have long juggled proprietary systems that cannot communicate with one another, creating operational silos, manual workarounds, and data integrity risks. The new system directly addresses this roadblock by having an open architecture, allowing for third-party instrument integration, and real-time oversight of integration capable unit operations from a single interface, notes a Cytiva spokesperson, who explains that the platform features include:

  • Native interoperability: The platform is natively integrated with Cytiva bioprocessing equipment and Rockwell Automation’s FactoryTalk software suite, enabling seamless interoperability across systems.
  • Scalable growth: A single platform expands from process development to commercial manufacturing without system redesign.
  • Cost-effective compliance: A streamlined digital manufacturing system reduces capital and operational costs and enables cGMP compliance.
  • Rapid implementation: Pre-engineered templates and modular design shorten deployment and validation timelines.
  • Enhanced operational insight: Centralized alarms, real-time monitoring, process intensification and batch reporting tailored to bioprocess workflows.

“This collaboration is designed to empower the next generation of biomanufacturers,” says Nicolas Pivet, manufacturing and digital solutions at Cytiva.

Industry data shows increasing demand for next generation process control systems as organizations transition toward data driven process intensification and continuous manufacturing. Equipment fragmentation remains one of the top pain points cited by biomanufacturers, particularly those advancing programs from R&D to clinical scale. By giving teams a unified digital control layer, the Figurate SCADA reduces the risk of human error, accelerates tech transfer, and supports reliable scaleup as workloads grow in complexity, points out the Cytiva spokesperson.

 

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