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Panelists:

Mirjana Efremova, PhD

Senior Lecturer
Queen Mary University of London

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Panelist

Mirjana Efremova, PhD

Mirjana Efremova earned a PhD in computational biology at the Medical University of Innsbruck, Austria, where she investigated the process of clonal evolution of cancer under immunosurveillance and how the immune system shapes tumor progression. She did her post-doctoral work at the Teichmann Lab, Wellcome Sanger Institute, where she co-led the development of the first human atlas of the maternal-fetal interface in early pregnancy using single cell transcriptomics. She also developed a cell-cell communication statistical framework CellPhoneDB (www.cellphonedb.org) for predicting enriched receptor-ligand pairs between the different cell types and inference of cellular communication networks.  In May 2020, she started a lab at Barts Cancer Institute, Queen Mary University of London (U.K.) within the Centre for Cancer Evolution. Her lab is focused on investigating the non-genetic mechanisms driving cancer initiation, metastasis, and therapy resistance.

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Phenotypic plasticity—the ability of tumor cells to switch states in response to their environment—drives colorectal cancer (CRC) progression, metastasis, and therapy resistance, but what controls it has been unclear. The application of single-cell multiomics and spatial transcriptomics in primary and metastatic tumor samples can reveal regenerative and inflammatory cancer cell states.

In this GEN webinar, Mirjana Efremova, PhD (Queen Mary University of London) will show how these technologies can be used to analyze primary and liver-metastatic CRC patient samples to reveal cancer cell states. These regenerative and inflammatory cancer cell states resemble metastasis-initiating cells in mouse models, are regulated by the transcription factors AP-1 and NF-κB, and sit at the invasive edge in an immunosuppressive niche. Improved understanding of these microenvironment-driven states and their regulators offers researchers new ways to target metastasis.

Webinar attendees will learn more about:

• The regenerative and inflammatory cancer cell states found in primary and metastatic CRC, including a hybrid regenerative/stem state, and how liver metastases re-establish the primary tumor’s heterogeneity;
• Transcriptional regulation of these states as well as signals from fibroblasts, macrophages, and T cells, and where the states sit in the tumor; and
• How to apply single-cell multiomics and spatial transcriptomics to link cell state, regulation, and tissue location, and identify candidate targets to limit plasticity and metastasis.

A live Q&A session will follow the presentation offering you a chance to pose questions to our expert panelists.

Produced with support from:

The post Single-Cell Multiomics: Phenotypic Plasticity in Colorectal Cancer Metastasis appeared first on GEN - Genetic Engineering and Biotechnology News.

Nabsys is positioning its Electronic Genome Mapping (EGM) technology as one way to close some of those gaps. At the American Society of Human Genetics (ASHG) 2026 Annual Meeting, held October 20–24 in Montréal, the Providence, RI-based company shared preliminary data on EGM in hematologic malignancies and previewed analysis software built for that application.

Bridging two disciplines

In a Nabsys-sponsored CoLab session on October 22, Alka Chaubey, PhD, FACMG, the company’s chief medical and genomics officer, presented “Electronic Genome Mapping in Hematologic Malignancies: Bridging Cytogenetics and Molecular Genomics” on behalf of Brynn Levy, MSc(Med), PhD, FACMG, of Columbia University. The talk covered early findings on how well EGM detects structural and copy number variants, including how its results compared with established cytogenetic methods.

The work builds on OhmX Platform installations at Columbia University Medical Center, awarded through the RAMP UP Program from Hitachi High-Tech America and Nabsys. Running on OhmX, EGM is designed to give a high-resolution, genome-wide view of structural and copy number variation. Nabsys frames the technology as a complement to existing tools rather than a replacement.

“Researchers need a more complete view of the complex genomic changes in hematologic cancers,” said Barrett Bready, MD, Nabsys’s founder and CEO. “These preliminary findings highlight EGM’s potential to complement established methods.”

Software shaped by users

Alongside the data, Nabsys demonstrated Nabsys Navigator, its cloud-based platform for EGM analysis and visualization. Navigator includes two pipelines: SV-Discover, for genome-wide structural variant analysis, and SV-Verify, for confirming structural variants flagged by other technologies. Booth visitors could also preview the company’s forthcoming hematologic malignancy analysis software, the same tool used to analyze samples in Chaubey’s presentation, and weigh in on its development. Bready said the company is “pairing that work with software development shaped directly by the researchers who will use it.” The OhmX Platform is for research use only and is not intended for diagnostic procedures.

The post ASHG: Nabsys Links Cytogenetics and Genomics to Map Hematologic Cancers appeared first on GEN - Genetic Engineering and Biotechnology News.

HUPO assembled 1,428 delegates from 45 countries under the theme “Proteomics Plus: Transforming Lives.” Across the September 27–October 1 program, conversations kept returning to questions that reach beyond the proteomics community: who will develop disease, why treatment fails, and whether a computational model can help us decide which experiment to run next.

The program itself reflected a field broadening its reach. Alongside the main scientific sessions were pre-congress workshops and training courses, short flash talks linked to poster sessions, and mentoring and networking for early-career researchers. The Early Career Researcher (ECR) program also featured manuscript, poster and three-minute thesis competitions. HUPO continued its interactive Bioinformatics Hub format, with 11 sessions spanning AI, multiomics and reproducibility. One, led by the Human Protein Atlas, explored integrating multiomics data and AI tools into its resources.

The exhibition hall and industry-supported seminars added another dimension. Thermo Fisher Scientific and Olink shared a booth for the first time, while Illumina showcased proteomics approaches for population-scale studies. Both hosted scientific seminars. Their presence underscored how technology development, data analysis and clinical research are increasingly part of the same conversation.

An invitation from China

Fuchu He, PhD, chairman of the National Center for Protein Sciences in Beijing, set out an ambitious direction in the opening keynote. He leads the China-initiated Proteomic Navigator of the Human Body, or π-HuB, whose published roadmap envisages investment running into billions of renminbi over three decades.

Jing Yang, PhD, π-HuB’s secretary-general and a researcher at Guangzhou National Laboratory, described an effort to profile organs, tissues, cell types, and individuals, building a reference for how human biology changes over a lifetime.

The ambition is to “predict the proteomic states of each person across their lifetime,” he explained, accounting for influences including the microbiome, immune system, environment, and lifestyle. Reliable individual predictions remain a research goal.

“We really want to collaborate with HUPO,” Yang told GEN, describing discussions about extending cooperation beyond conferences and communication into science and funding.

Building a cell, together

Tiannan Guo, PhD, a professor at Westlake University, connected spatial proteomics and temporal perturbation proteomics with the development of AI-based virtual cell modeling. His presentation described proteomic work spanning 58 major tissue types, 251 more finely defined tissue types, and 25 carcinoma types, as well as a perturbation proteomics-based virtual cell model for triple-negative breast cancer with potential clinical benefits.

Guo’s virtual-cell framework brings together existing biological knowledge, cellular architecture, and dynamic measurements of responses to perturbations. Models would guide experiments, and the resulting measurements would test and refine their predictions.

The Westlake AI Virtual Cell–Yeast, or WAY, project divides that challenge into interconnected cellular functions, including metabolism, membrane systems, and stress responses. Task-specific AI agents would work together.

Contributors to the published framework include researchers affiliated with ETH Zurich, Stanford University, and the University of Toronto, among others. 

Cells have to cope with lunch

Florian Rosenberger, PhD, a SciLifeLab fellow and assistant professor at Karolinska Institutet, gave an example of what a real cell must handle.

“Life is all about being able to deal with the little fluctuations,” he told GEN, including “being able to deal with lunch without dying instantly.”

His single-cell deep visual proteomics work investigates mitochondrial disease while preserving tissue context. Access to oxygen and nutrients varies between neighboring cells, as can the accumulation of mutations. Rosenberger compared the patchwork of cellular states to a Portuguese tile floor.

By isolating cells in different functional states and arranging them in an inferred sequence, or pseudotime, his group can investigate proteomic changes associated with deterioration. This reconstructs a possible progression rather than following the same cell through time. The aim is to distinguish responses associated with failure from those that might support resilience. Rosenberger stressed that this is early work, but understanding those responses could eventually inform research in other diseases involving mitochondrial dysfunction.

“What is the normal working range that a cell can have, and when does it tip over into disease?” he asked.

Fabian Coscia, PhD, head of the spatial proteomics group at the Max Delbrück Center in Berlin, described how cancer cells can differ in their response to treatment. Rare cell populations may carry protein states associated with resistance before treatment begins; others adapt during treatment. Comparing pretreatment and post-treatment samples helps investigate both possibilities.

In Alzheimer’s brain tissue, his group found that neurons with visible tau pathology and neighboring cells without it did not separate neatly into affected and unaffected categories. Proteomic measurements revealed intermediate states.

“You have an entire trajectory of states,” Coscia said. These are precisely the distinctions a predictive model will need to capture.

From tissue to accessible samples

Birgit Schilling, PhD, a professor at the Buck Institute for Research on Aging, spoke with GEN in Toronto the previous year. This year in Singapore, she described how her laboratory was now using spatial mass spectrometry to investigate human osteoarthritis, finding localized changes in bone that bulk analysis could obscure.

“What I’m particularly excited about is that we took that to then bring that back to a biomarker fluid,” she said. The team is following tissue-level discoveries with biomarker work in synovial fluid, the fluid within the joint.

Mitchell Lai, PhD, a senior lecturer in pharmacology at the National University of Singapore, examines Alzheimer’s biomarkers in an Asian cohort with substantial cerebrovascular disease, testing their usefulness beyond the Western populations that dominated earlier studies.

“Now we are more interested in prognostic biomarkers,” Lai said. He wants measurements that help anticipate future dementia and cognitive impairment.

His work with the NULISA technology, explores brain-derived phosphorylated tau. The assays recognize both a phosphorylation site and a brain-associated splice variant, distinguishing a more specific signal within circulating tau.

Lai emphasized that longitudinal research requires comparable assays even when samples are collected years apart.

Jochen Schwenk, PhD, who chaired HUPO’s Plasma Proteome Project over the past decade, noted a substantial increase in abstracts describing studies of human biofluids in large cohorts. He welcomed the trend but cautioned against drawing conclusions too early without considering findings from similar studies. He also highlighted growing interest in microsampling, which could make sample collection easier and broaden the role of proteomics in precision medicine.

Gabriel Mazzucchelli, PhD, head of the omics group in the University of Liège’s mass spectrometry laboratory, is exploring a different sample: sweat. His team has developed a spectral library containing around 4,000 proteins and conducted pilot work on treatment monitoring. An overnight collection patch under development could make repeated sampling possible without hospital visits.

Mapping protein forms and functions

The separate, closed Day of the Research Initiatives brought π-HuB, the Human Proteome Project, and the Human Proteoform Project together to discuss the field’s future.

Neil Kelleher, PhD, director of Northwestern University’s Chemistry of Life Processes Institute, described the Human Proteoform Project’s ambition as having “the genome project moment for proteins.” Representing the Consortium for Top-Down Proteomics, he proposed a reference set of intact molecular forms of proteins across human cell types and body fluids, estimating 50–100 million distinct proteoforms.

His presentation argued that distinguishing those forms could improve biomarker selection, patient stratification, and drug-target decisions. “It is a structural project more than a functional one.”

The proposed reference would require roughly 10 years and $300 million–$500 million annually, he estimated.

Cecilia Lindskog, PhD, an associate professor at Uppsala University and chair of HUPO’s Human Proteome Project, described the complementary challenge.

“We want to determine the function of every human protein,” she said, noting that fewer than a third have well-characterized, experimentally validated functions. She wants shared standards, complementary methods, and potentially shared samples and joint pilot studies to help close that gap.

The Human Protein Atlasintroduced a helpdesk at the congress alongside a Bioinformatics Hub session on integrating multi-omics data and AI tools.

Coscia described workshops bringing imaging researchers, the Human Protein Atlas, and the mass spectrometry community together around data formats and integration. He called for better protocols, interlaboratory comparisons, and metadata that make experiments reproducible. He also cautioned that exploratory spatial proteomics remains largely a discovery technology; some findings may reach clinical practice through established methods such as immunohistochemistry.

Beyond the congress

At the gala dinner in the Flower Dome at Gardens by the Bay, there was plenty to discuss. Singapore’s population studies, China’s international initiatives, and the clinical questions running through the conversations gave “Proteomics Plus” substance: more ways to measure biology, more people contributing, and more demanding questions about what the results mean.

By the next HUPO, it would be great to hear how those models performed on new data and whether candidate biomarkers held up in larger, more diverse patient cohorts. The next step is to show not just what proteomics can measure, but which experiments and clinical decisions those measurements can improve.

The post World HUPO 2026: Asia-Pacific Ambition and the Road to Predictive Biology appeared first on GEN - Genetic Engineering and Biotechnology News.

Anthropic’s Form S-1 Registration Statement, filed Monday with the U.S. Securities and Exchange Commission, did not reveal key details such as what research areas it is pursuing through the molecular biology lab it disclosed last month, and what if any new therapies it is seeking to discover and develop. Nor did it quantify Anthropic’s total spending on life science activity.

The IPO filing also did not say how much money Anthropic seeks to raise on Wall Street—how many shares at what price—though news reports have had the company pursuing an eye-popping valuation of $2 trillion, 13% above the $1.77 trillion valuation of Elon Musk’s SpaceX when it went public in June. Anthropic plans to begin trading public shares on the Nasdaq Global Select Market under the ticker symbol ANT.

However, the filing said that Anthropic is creating AI agents based on its Claude family of large language models that focus beyond accelerating drug discovery and development, toward handling many of the physical tasks associated with creating new therapies.

“Claude is used in drug discovery and development by our customers, our biotechnology partners, and our own researchers. We are building AI agents that support scientists at each stage of that process,” Anthropic stated.

“Beyond knowledge work, we believe frontier AI has the power to transform physical work. Advances in Claude’s general capabilities, together with our Model Hardware Standard, now allow it to operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms.”

In August, Anthropic publicly previewed its Model Hardware Standard (MHS), which began as a collaboration between Anthropic and Howard Hughes Medical Institute (HHMI) Janelia Research Campus. Anthropic described HMS as “a shared specification for AI agents to safely operate physical devices, to a first group of scientific research labs and advanced manufacturers.”

“Currently in research preview, our Model Hardware Standard enables AI agents to find, understand, and safely operate lab and manufacturing equipment such as microscopes, liquid handlers, and off-the-shelf robotic arms,” Anthropic explained in its IPO filing. “Our Model Hardware Standard is model-agnostic and lets any agent harness reach devices with a programmable interface through standard protocols such as MCP.”

AI workbench

And in June, Anthropic released Claude Science, an AI workbench for scientists that consolidates fragmented research tools, including more than 60 scientific databases and connectors pre-configured for genomics, proteomics, structural biology, and more, into a single reasoning layer. One Anthropic partner, Basecamp Research, has made available through Claude Science its antibiotic design and vaccine target prediction EDEN models for global public health research.

In the IPO filing, Anthropic also summarized many—though not all—of its recent life-sci activities: “To build the best tools for the industry, we have invested in deep life sciences expertise of our own, hiring industry experts, building a molecular biology lab, acquiring Coefficient Bio, and partnering with industry leaders.”

Those efforts have paid off, the company said: As of September 30, 2026, Anthropic included among its customers “nine of the 10 largest” pharmaceutical companies, without disclosing their names. Those 10 are among the over 50% of the Fortune 500 that Anthropic counts among its customers with $100,000 or more of revenue on a TTM [trailing 12 months] basis. Anthropic added that every company in the Fortune 10, and more than 30% of the Forbes Global 2000 were also among its customers with $100,000+ revenue TTM.

Anthropic is carrying out life sciences research through a molecular biology lab and a research group formed earlier this year “to see whether general AI models can systematize and accelerate discoveries capable of revolutionizing biology and medicine,” the company stated last month. “Our focus is on fundamental biology research using Claude: exploring datasets of DNA to identify uncharacterized protein families, generating hypotheses at scale, and testing them through experiments in the lab.”

The IPO filing offered no details about how many researchers are based at the lab, and how much R&D spend the lab accounts for of Anthropic’s total research and development expense. That expense jumped more than quintupled during the first six months of this year, to $9.741 billion from $1.85 billion in January-June 2025. For all of last year, Anthropic spent a total $6.338 billion on R&D, according to the Form S-1.

The research lab generated worldwide headlines last month when Anthropic announced that its Claude large-language model “discovered a novel enzyme system with properties reminiscent of CRISPR, with only high-level direction from our scientists.”

Elaborating further in a post on X, Anthropic CEO Dario Amodei described the novel enzyme system as “a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student.”

‘Something interesting’

“The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out,” Amodei added.

In April, tech news outlets The Information and the Substack-based Newcomber newsletter founded by Eric Newcomber first reported that Anthropic acquired the stealth AI biotech startup Coefficient Bio for about $400 million in stock. Anthropic confirmed the acquisition though not the price in its IPO, saying it bought the company “to accelerate our research in the life sciences.”

Founded last year, Coefficient has developed an AI platform for purposes that include planning drug research and development, managing clinical regulatory strategy, and identifying new drug opportunities, OncoDaily reported. Coefficient was established by co-founders that include Nathan Frey, PhD, and Samuel Stanton, PhD, both previously with Genentech’s Prescient Design computational drug discovery unit. Another co-founder, Aris Theologis, serves as CEO and was previously chief business officer at Evozyne and a vice president at Paragon Biosciences.

According to the IPO filing, Anthropic finished January-June 2026 with a staggering net loss of $80.748 billion, up more than six-fold from the $12.969 billion net loss the company reported for the first six months of 2025. However, revenue leaped 14-fold to $16.286 billion in the first half of this year, from $1.163 billion in January-June 2025.

And Anthropic’s operating loss, defined as operating expenses exceeding revenue from core business operations, actually shrunk during the first half of 2026 to $693 million from $2.135 billion a year earlier.

For all of 2025, Anthropic reported a net loss of $41.965 billion, up five-fold from the $8.309 billion net loss of 2024, itself more than five times the $1.531 billion net loss of 2023.

Goldman Sachs and Morgan Stanley are co-leading the IPO, with participation by 20 other banks and other investment institutions.

The post Anthropic IPO Discloses Plans for Life Sciences Growth appeared first on GEN - Genetic Engineering and Biotechnology News.

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Panelists:

Bernd Bodenmiller, PhD

Associate Professor
Dept. of Quantitative Biomedicine
University of Zürich
ETH Zürich, Switzerland

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Panelist

Bernd Bodenmiller, PhD

Bernd Bodenmiller, PhD, is a quantitative biologist who develops novel experimental and computational approaches for the quantitative analysis of tumor ecosystems to improve our understanding of the mechanisms of tumor development. He is the founding director of the Department of Quantitative Biomedicine (DQBM) at the University of Zürich, which fosters research and education at the interface of biomedical research, biotechnology, and computational biology to guide development of next-generation precision medicine. His group has pioneered the development of imaging mass cytometry, which enables the simultaneous imaging of dozens of proteins and transcripts in tumor tissues and the histoCAT software toolbox. His group applies these methods to unravel how cells in the tumor ecosystem drive cancer development to identify mechanisms that might be exploited for therapeutic targeting.

Bodenmiller obtained his PhD in the group of Ruedi Aebersold, PhD, at ETH Zürich. For his postdoctoral training, he joined the laboratory of Garry Nolan, PhD, at Stanford University. In 2012, he became a group leader and in 2013 an SNF/ERC assistant professor at the University of Zürich. In 2019, he was tenured and became the founding director of the DQBM. In October 2020, Bodenmiller was appointed as Dual Professor for Quantitative Biomedicine at UZH and ETH Zürich.

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Tumors are complex cellular ecosystems in which cancer cell phenotypes, their spatial organization, and their interactions with immune and stromal compartments determine disease progression and therapy response. To resolve this complexity, the use of highly multiplexed Imaging Mass Cytometry (IMC) together with multiomic profiling and artificial intelligence (AI) can identify clinically relevant features of the tumor microenvironment, providing a rich framework for patient stratification and targeted therapy in precision oncology.

In this GEN webinar, Bernd Bodenmiller, PhD (University of Zürich), illustrates this integrated approach through two recent examples. In a study of 323 epithelial ovarian cancers, tumors with more diverse cell phenotypes and spatial organization recurred faster. His lab also identified a therapy-resistant tumor cell phenotype (TUBB3+/pRB+) associated with recurrence and poor response to standard therapy. Spatial transcriptomics and ex vivo drug screening of 3D sphere models and patient samples showed that these cells resist standard-of-care agents but are sensitive to specific inhibitors. In 498 clear cell renal cell carcinomas, the use of AI revealed three survival ecotypes that predict survival better than basic clinical data and molecular stratification models. Each ecotype has its own genomic and metabolic features and is predictive of treatment outcome. Deep learning predicted the ecotypes directly from routine H&E slides, and the findings were validated across five cohorts comprising more than 2,500 patients.

Webinar attendees will learn how integrating spatial single-cell proteomics by IMC with multiomics and AI identifies therapy-resistant cell populations and prognostic tumor ecosystems. This approach provides a detailed framework for patient stratification and targeted therapy in precision oncology.

A live Q&A session will follow the presentation offering you a chance to pose questions to our expert panelists.

Produced with support from:

The post Spatial Proteomics for Precision Oncology appeared first on GEN - Genetic Engineering and Biotechnology News.

This is motivating a biotech revolution building up in South Africa—the continent’s most industrialized nation where startups are confronting “pharmaceuticals apartheid”—tinkering with low-cost, decentralized biotech R&D and manufacturing prototypes.

So much is at stake and urgent, said Nick Enslin, co-founder and COO of Immobazyme, one of the flagship South African startups that specializes in advanced biomanufacturing and precision fermentation with the goal of making affordable, high-value recombinant proteins, enzymes, and growth factors to help slash Africa’s biopharma import bill.

“One of the biggest things we realized as a country, but also as Africa, is that we’re importing 99% of our biologics and drugs. For the COVID-19 vaccine, we were the last group of people to get this vaccine,” he lamented. A cursory look at the South African Clinical Research Association, the equivalent of the FDA, shows they still follow the 2014 FDA guidelines, he said. “And that’s quite old,” he told GEN.

As a result, they would need to update their regulations to understand the science better, what biosimilars are, what mRNA vaccines are, and why the science is moving so fast that regulators are struggling to keep up.

Africa has “many opportunities” to domesticate its medical biologics supply chains, but reality is daunting, said Enslin, citing a recent “disruption” at Aspen—Africa’s largest generic drugs maker—which threatens an oral contraceptives shortage in South Africa.

Part of the reason is that GMP manufacturers of biosimilars, specifically, are far and few between on the African continent.

“In South Africa, we’ve only got a handful, and most of them are looking at small molecules, chemicals, and stuff like that, necessarily creating bio-biologics,” said Enslin.

The mission

The mission is clear for Immobazyme, which recently captured the limelight after raising $1.45 million in venture funding (bringing the total the company has raised to $2.9 million), which is modest by Silicon Valley standards but significant in South Africa’s limited venture finance ecosystem.

“Having our own GMP facility, manufacturing biosimilars for the African continent, that’s where we want to be by the end of the 2030s,” said Enslin, who is talking about “lower hanging fruit” for Africa’s biotech innovators to grab and accelerate through development and into the market.

He added that he is referring to “nutraceuticals, cosmetics, food and beverages, and agribiotech, which are subject to lower regulation and certification than drugs and other medicines. But it’s still the same premise that the African continent is struggling with these kinds of novel products.”

His first patent came about when he was a student at South Africa’s Stellenbosch University, the premier South African and African research corridor in the Western Cape Province, where billionaires live adjacent to bubbling ideas in biotech, banking, and software.

“We realize that these enzymes, whether it’s in an industrial application or mRNA manufacturing, are relatively expensive. And you use them once, and then you can’t recover them. You can’t reuse them again even though they’re perfectly fine to reuse,” he said.

So, they developed a method to immobilize enzymes on a solid metal matrix, allowing substrates to pass through and undergo enzymatic reactions while the enzymes remain in place for repeated use, he explains, giving an example of lactose-free myelin oligodendrocyte glycoprotein (MOG).

“What they do is they immobilize lactase, which is an enzyme that breaks it down into simpler sugars. And they passed the milk over and got lactose-free milk. But the current enzyme immobilization technologies that were out there at the time were either quite expensive or they weren’t optimized for multiple different kinds of applications. So, this is where PepTrap came in,” Enslin said, referring to the proprietary, synthetic cellulose-based enzyme immobilization technology developed by Immobazyme that traps and stabilizes enzymes, allowing them to be reused cost-effectively across multiple industries.

Getting the science right was the easier part for Enslin and his two co-founders. Scaling the science into any kind of business “is just really tough, but scaling a biotechnology company in South Africa is even tougher. It’s capex heavy. It’s infrastructure heavy. And then every time you 10x your manufacturing capability, you have to basically buy a completely new production pipeline to meet that scale,” he said.

The hub

For decades, South African universities have been excellent at generating world-class research papers, but much of that IP stays stuck in academic journals. LaunchLab CERIBIO is a 300 m² biotechnology laboratory and business incubator at Stellenbosch University and looks to actively disrupt this “publish or perish” culture in South Africa. Enslin’s Immobazyme startup is one of the most successful spinouts from this incubator.

“It is very challenging to do commercial work in a university laboratory,” said Anita Nel, chief director of innovation and commercialization at Stellenbosch University. Hence, they have created this space to ensure that the university’s biotech spinout companies have a dedicated, shared space. They actively encourage their researchers to publish their results once CERIBIO has the opportunity to protect their inventions, as publications also bring credibility to their research. “It is more a case of patent before you publish than preventing publication at all,” she added.

When a university researcher comes to LaunchLab CERIBIO with a breakthrough, there is a filtering method to ensure the presentation can translate into commercial success. “We perform due diligence on every new invention that is disclosed to us to make very sure that there is commercial potential, that it is novel, and to determine what the best route to market is—either a license to a company with an existing market or a new spinout company with the support of the university,” she told GEN.

The spinouts from LaunchLab CERIBIO are also supported to de-risk the ventures further and are connected to both local and global investors who can provide funding to help them progress faster. “These investors understand the deep-tech and biotech space and feel comfortable investing in this sector, without expecting the fast progress that you typically see in fintech,” she added.

Nod of approval

Immobazyne’s recent venture financing success is a riposte to critics who for a long time have argued that Africa’s biotech can’t attract this kind of liquidity. It’s a massive nod of approval, Enslin explained. “I think that’s a testament to the kinds of companies that are coming out of the African continent and that resilience and the determination. There have been some amazing success stories coming out of South Africa,” he said, pointing to South Africa’s Kapa Biosystems, which was acquired for a record $445M by Roche in 2015.

“It’s an affirmation that we can actually build something incredible here. I think it’s heating the ecosystem here on the ground in the Western Cape specifically. I mean, they’re calling us the Silicon Valley or the Cape Silicon Valley,” he said with humor.

The post South African Labs Strive for Africa’s Biopharma Sovereignty appeared first on GEN - Genetic Engineering and Biotechnology News.

That’s the opinion of Nicolas Pivet, vice president and general manager of manufacturing and digital solutions at Cytiva. Major scientific advances have enabled the development of truly amazing therapeutics such as RNA technologies and cell and gene interventions, yet manufacturing innovation hasn’t kept pace. “Our ability to manufacture these therapies reliably, repeatedly, and at scale remains a major constraint,” Pivet tells GEN.

“The industry often focuses on symptoms [of biomanufacturing bottlenecks] such as paper batch records, disconnected laboratory information management systems, fragmented automation architectures, isolated enterprise resource planning deployments, or data silos. Those challenges are real, but they are not the root cause,” Pivet stresses.

“The deeper issue,” he says, “is that many manufacturing environments evolved over decades and were never designed as connected systems. Digital capabilities were often layered on to solve local problems rather than integrated around end-to-end manufacturing performance. The result is an environment that is increasingly data-rich but…insight-poor.”

In that environment, organizations can’t extract actionable insights early enough to affect outcomes. They have a visibility problem.

Focus on observability

The first step in resolving the bottlenecks is to solve specific problems.

“You cannot control what you cannot see,” Pivet says. He advises industry to focus first on improving observability. Gaining insights into process deviations early enables earlier intervention, which creates better decisions and improves confidence in those decisions, he explains. This is the foundation of advanced manufacturing.

“Then connect the data, workflows, and decisions required to improve the outcome,” Pivet advises. “Biopharma does not lack information. It lacks accessible, contextualized, and trusted information,” he says. “Connecting process, equipment, analytical, and workflow data establishes the foundation required for predictive analytics, advanced automation, digital twins, and AI.”

“Only after that [has occurred] should advanced control or AI-enabled decision-making become part of the discussion.”

Therefore, rather than starting with AI use cases—as many try—organizations should first focus on observability, and then address interconnectivity, data integrity, and maintaining operational context for those data flows. When those are resolved, AI can be added to the mix. “AI does not fix weak foundations,” he says. “It exposes them.”

Face organizational barriers

The barriers to digital transformation are more often organizational than technical. That’s because biomanufacturers consider change riskier than maintaining current practices. In 2025, “more than a third of biopharma companies were still in early exploration or had not started their digital transformation journey,” he says, underscoring his point.

Trust is typically the rate-limiting step, and that is established only by reliable performance. Therefore, “the organizations moving fastest are those demonstrating value on real manufacturing problems, validating outcomes, and gradually scaling successful approaches rather than attempting sweeping transformations,” Pivet observes.

Manufacturing equipment in the future will not just be smarter; it will be designed from the outset as elements of connected, interoperable systems that provide early visibility and easy integration into automated and connected workflows.

Prepare the workforce

“The conversation around AI often focuses on technology. The more important conversation is about people,” Pivet emphasizes.

Automation, agentic and generative AI, and other smart manufacturing technologies are changing roles throughout the workforce at, seemingly, warp speed. Everyone, from equipment operators, process engineers, data scientists, and quality professionals up through the executive suite, is coming to grips with new ways of working that require new skills and new ways of thinking.

“Data literacy, systems thinking, automation fluency, model interpretation, and cross-functional collaboration are becoming increasingly important,” Pivet points out. Consequently, people are spending less time collecting information and reacting to events, and more time interpreting data and optimizing processes.

The workforce, however, is still in transition. “The Global Biopharma Index reported that 29% of organizations experience severe shortages in digital and AI-related skills,” he says.

“Workforce readiness is one of the defining challenges of this decade,” Pivet continues. “The most successful manufacturers I encounter are not necessarily the ones deploying the most advanced technologies. They are the organizations creating confidence around new ways of working.”

Within five years, he predicts a plethora of hybrid roles that blend both process and digital expertise. Future titles may include digital process engineer, manufacturing data scientist, process intelligence specialist, and manufacturing systems architect, he adds.

Filling those new roles means preparing the workforce today. He advocates upskilling individuals enterprise-wide, ensuring the various functional teams learn together, rather than treating digital transformation as the purview of any particular group.

Even today, across industries, traditional learning programs are being complemented by immersive learning experiences. Biopharma will be no different, especially given its growing complexity and limited opportunities for hands-on learning in the face of good manufacturing requirements, production schedules, and equipment availability.

Immersive learning experiences involving mixed reality and augmented reality “allow operators, technicians, and engineers to interact with equipment and workflows, and troubleshooting scenarios in highly realistic digital environments,” he says. By accelerating learning, enhancing retention, and building confidence, workers are better prepared when they enter live manufacturing environments.

Eventually, Pivet says, “Immersive learning may become as important to workforce development as flight simulators became to aviation training.”

Ease tech transfer

The tech transfer process also has a major bottleneck, but it has nothing to do with technology or human factors.

“The most common reason transfers fail is not because process instructions are missing,” he says. “It is because process understanding fails to transfer with them. Receiving organizations often inherit process descriptions, specifications, and operating procedures, but not the context explaining why those parameters matter or how the process behaves under changing conditions.”

Those gaps are becoming ever more important as therapies become increasingly complex. Successful tech transfer, therefore, includes transferring process understanding. Digital twins help minimize the gap between process instructions and process understanding by enabling teams “to test assumptions virtually before testing them physically,” Pivet says, thus enhancing success. In this way, “…uncertainty is removed rather than relocated.”

Lead with digital change

If you can prioritize only one area for improvement, Pivet advises choosing digital infrastructure because, he explains, “people and processes increasingly depend on information. Digital infrastructure creates the conditions for successful technology transfer, effective workforce development, predictive analytics, process understanding, and operational improvement. Without trusted information flows, organizations struggle to scale knowledge, replicate success, or improve decision quality.”

High-quality information flows, he predicts, will be a major determinant of manufacturing competitiveness in the coming decade. “The companies best positioned to lead the next era of biomanufacturing will be those that combine AI initiatives with strong connected-data and observability foundations,” he suggests. By doing so, they will be better able to translate scientific innovation into predictable, scalable, operational performance.

“Biopharma does not have an imagination problem,” Pivet reiterates. “It has an execution problem. Closing that execution gap is one of the greatest opportunities our industry has to accelerate patient access, improve resilience, and unlock the full value of the science being developed today.”

The post Overcoming Bottlenecks: Biopharma’s Big Challenge Is Execution appeared first on GEN - Genetic Engineering and Biotechnology News.

Headed by ICREA Research Professor Luciano Di Croce, PhD, a researcher at the CRG, the team created a scoring system by moving repeatedly between laboratory experiments with mice and patient records, crossing the two to look for genes that behaved consistently in both.

The investigators had initially focused on a gene called PHF19, which the Di Croce lab has studied for more than a decade and is thought to suppress blood cancer formation. PHF19 belongs to a family of genes that keep other genes switched off, a system that cells use as they progress along the differentiation process from immature to mature cells.

Through their research the team found six genes that reliably predict AML patient survival. Some genes—CARMIL1, HOPX, and ALDH1A1—were more active in patients with poor outcomes while others—CALR, STAR, and PHF19—were less active. The collective activity of all six genes, which the researchers called the PHFScore, divided patients into groups with sharply different survival prospects.

Findings from the study could point to new ways of helping to predict outcomes in patients with this type of blood cancer, contributing to the development of future prognostic tests, and may suggest new therapeutic targets. Patients suspected of having AML already give a bone marrow sample for laboratory analysis, and measuring six genes is simpler and cheaper than reading a broader genetic profile.

“PHF19 on its own already tells us something about how patients will fare, but combining it with five other genes is a dramatic improvement which gives us more accurate information for AML prognosis,” said Di Croce.

Gianni Gamarra Figueroa, PhD, added, “Some of the genes have never been linked to blood cancer before and could also be potential new therapeutic targets. These genes haven’t been described in leukemia or even in how blood forms. We don’t yet know what they are doing there, which is exactly what makes them worth exploring.”

Di Croce is senior author and Gamarra Figueroa is first author of the team’s published paper in Science Advances, titled “Polycomb factor PHF19 affects AML initiation, patient prognosis, and survival.” In their paper the authors concluded “By integrating our in vitro multiomic data with the molecular and clinical data from various AML datasets, we successfully identified a molecular signature—the PHFScore—to identify patient prognosis.”

Acute myeloid leukemia is “… a heterogeneous malignancy characterized by diverse genetic and epigenetic alterations,” the authors wrote.  Earlier work in the Di Croce’s lab showed that mice lacking the PHF19 gene manifest a condition resembling myelodysplastic syndrome (MDS), where blood cells fail to mature properly and build up in the body without yet becoming cancerous. “We previously showed that the Polycomb-associated factor Phf19 is required for hematopoietic stem cell (HSC) maintenance and state control in mice and that its loss (Phf19^KO) impairs HSC function,” the team continued.

“Leukemia develops in stages, as a cell gradually accumulates defects,” explained Gianni Gamarra, PhD, “Losing PHF19 doesn’t cause leukemia on its own, but it is already a push down that road.”

The new study asked what happens when leukemia originates in cells that have similar starting conditions—not cancerous, but not quite healthy either. The researchers introduced a leukemia-causing gene into mouse blood cells with and without PHF19. Cells lacking the gene formed more colonies in the lab and, when transplanted into mice, took hold more easily. They also stayed locked in an immature, stem-cell-like state.

“Blood cells form a hierarchy,” added Gamarra Figueroa. “At the top are stem cells, which are still plastic and could become almost anything. As they move down, they mature and take on a fixed identity. The more a cancer holds on to that plasticity, the more malignant it tends to be.”

The plasticity made the cells harder to treat. Cells without PHF19 largely ignored the effects of chemotherapeutic drugs which are commonly used to push leukemia cells into a mature state, one of the strategies doctors use against a disease in which cells become stuck partway through development and multiply unchecked. “Phf19 deletion promoted acquisition of stemlike features and reduced responsiveness to differentiation-inducing AML therapies,” the team stated.

To check whether their mouse model reflected human disease, the researchers compared which genes were switched on and off in both the mouse cells and from data generated in human studies. The same pattern appeared in patient datasets. “Molecular and functional consequences of Phf19 loss paralleled those observed in the datasets of patients with AML,” they further noted. “Patient data from different AML datasets allowed us to determine that patients with low levels of PHF19 expression had a stemlike transcriptomic profile, which, according to the literature, is linked to more chemoresistant leukemias.”

Among younger adults treated for AML, those with the lowest levels of PHF19 survived for less time than those with the highest. they were also less likely to go into remission after standard chemotherapy. Fewer than half did so, compared with up to 85% of patients with the highest levels.

“The patients have advanced leukemia, so we are comparing mice and humans at very different stages of the disease, yet the molecular data point in the same direction, which gives our model a great deal of strength,” said Gamarra Figueroa.

The post Predicting Leukemia Survival May Be Aided by Six Gene Scoring System appeared first on GEN - Genetic Engineering and Biotechnology News.

That bounceback helps explain the surge in initial public offerings (IPOs) this year vs. 2025–27 offerings totaling $7.776 billion, up a sizzling 573% from $1.155 billion in 11 offerings in all of 2025. During the third quarter, nine IPOs totaling $2.4 billion reached Wall Street, the largest number in five years, according to investment bank Locust Walk.

“Yes, we’ve absolutely had a comeback year,” declared Geoff Meyerson, CEO and co-founder of the investment bank Locust Walk. He is also co-founder and CEO of Banyan BioInnovations, an investment firm launched in May to nurture startups by identifying best-in-class or first-in-class clinical-stage assets, forming new companies, or “NewCos,” around them, then providing experienced, centralized support to help them accelerate drug development.

In its recently released 2026 Q3 Market Conditions Report, Locust Walk tracked measures showing growing strength yet continuing challenges for biotech companies during the third quarter:

• Global licensing deals fell to a two-year low of about $48 billion in 41 transactions, from $71.5 billion in 43 transactions during Q2, with the average deal declining to $1.171 billion from $1.662 billion as deals focused more on clinical-stage assets than milestone-heavy discovery/preclinical assets.
• China leads in licensing, accounting for 58% of 2026 value through Q3, up from 48% a year ago, while the United States placed second at 26%. Europe and the United Kingdom slipped to 8%.
• Mergers and acquisitions (M&A) totaled $42.1 billion in 30 transactions, down from a three-year high of $80.7 billion in 35 transactions during Q2. However, total M&A value of $173 billion during Q1–Q3 2026 is already larger than all of 2025.
• PIPE (private investment in public equity) activity grew to $3.808 billion across 14 deals, well over the $603 million of Q2 and $1.151 billion of Q3 2025.

Meyerson addressed the growth of U.S.-Chinese licensing/collaboration deals, the recent growth of IPOs, and the still-chilly climate for startups seeking financing in a recent interview with GEN Edge (edited for length and clarity).

GEN Edge: Public stock markets and private VC markets have shown strength this year, yet we’re seeing plenty of companies avoid them through licensing and collaboration deals, many reaching multiple billions of dollars. Why have licensing-collaboration deals continued to grow despite improving markets?

Geoff Meyerson: It’s a good question. It’s a complicated answer. In the United States, most licensing deals are early-stage discovery and preclinical. It’s rare to do a clinical stage license in the United States, because typically, if you want to get access to U.S. rights as a pharma company, you have to buy the company. In the United States, companies won’t do a license unless it’s a non-core asset. That’s why the M&A statistics show that 91% of total deal value was for U.S.-headquartered companies. The difference, though, is where global licensing deals are derived from: 58% of the total value is coming from China, compared to 26% from the United States.

GEN Edge: Why?

Meyerson: Because for Chinese companies, the Hong Kong markets have effectively shut down. That was a very popular route last year to raise capital. Now, because of red chip and other policies, it’s becoming significantly harder to raise capital in Hong Kong, and a lot of Chinese companies are in line, and the government makes the decision as to whether or not a company can go public.

If you also look at the M&A statistics out of China, approximately 1% of the total deal value is from M&A. If you can’t get liquidity as an investor via going public, and you can’t get an exit via M&A, how else can you either support your company or potentially return capital to the shareholders? The only other option that I’m aware of is licensing. The licensing deals have become the lifeblood and the source of revenue or capital for Chinese companies. That’s why that trend is so material.

In many cases, you can even return the capital to your investors from a license, which in the United States would be double taxed, highly inefficient; that’s why you don’t see a lot of that happening in the United States. The difference also is that the upfronts in China are typically lower as a percentage of the total deal value compared to the United States, because in China, optics matter more. In the United States, the share of the expected net present value matters more. The Chinese companies don’t value the royalties as much. They value very large milestones that in many cases will never be achieved, so that inflates the total deal value number, and that’s one of the reasons why the number is so skewed in China’s favor.

GEN Edge: How concerned are you about Washington, both the Trump administration and Congress, tightening the rules for U.S.-Chinese collaborations?

Meyerson: It’s an interesting question and interesting timing for it, because [on October 16], the Chinese government made a major proclamation about not being able to transfer certain technology platforms outside of China. There’s just as much, if not more, risk in the actions of the Chinese government compared to the U.S. government. In China, one-party rule can make a decision, and it happens. In the United States, you need Congress, you need legislation. Yes, there could be executive orders, but it’s not the same force and permanence. So, there’s definitely geopolitical risk.

My instinct is that it’s not going to be something that is going to be shut down, ever. There will always be a way around it. I’m not going to cover that here, but all kinds of strategies can be deployed to minimize some of the regulatory barriers. Undoubtedly, it’s going to get harder before it gets easier, and it’s going to be driven by both governments, not just the United States.

GEN Edge: What effect has the growth in U.S.-Chinese licensing collaborations had on the market and public company stocks?

Meyerson: I think the U.S. companies that don’t know their competitive set out of China run the risk of having their technology being surpassed from a data perspective. And what’s changed is a lot of U.S. companies have decided not to publish their early-stage pipeline, they’re not publishing patents early. They’re waiting till the last minute, because as soon as that patent is filed, 18 months later it publishes. And the second it publishes, tons of AI bots are circling, figuring that out, and within a week or two, you can design chemical matter or biology around it.

I’m not so sure that market capitalization of the companies is being impacted, but I do think that whenever there’s positive data in the United States, that spurs lots of Chinese companies to push harder. If there’s positive data in China, and that can be read through to the United States, it’s just like any competitor announcing data can impact the stock price of your drug if it’s in the same class, if you haven’t fully differentiated from everyone else. Is it a class effect? Or is it something that is unique to a specific drug? I don’t know if there are direct impacts of all this competition, other than data emerging that, just like any other data coming out in a class and having an impact.

GEN Edge: This year has seen one pause in biotech IPO activity just after the Iran war started, after Generate Biomedicines went public. The markets heated up again in the spring through summer, but have held back in recent weeks. How much of that is investors and others waiting to see what Anthropic does with their IPO, or are there other reasons for the pause lately?

Meyerson: My guess is that the recent swoon with the specialists has really caused an issue. That’s number one. And number two, with the inflation print and the increase in rates on long-dated treasuries, that has a real negative impact on biotech. My instinct is, the markets will recover for the specialists. Who knows how long the rates will be higher on the long-dated bonds, but I think it will be coming back. I don’t think the cycle is over.

GEN Edge: What effect is Anthropic’s IPO having on AI-focused biotech IPOs?

Meyerson: I’m not so sure AI biotech IPOs are actually meaningful, because at the end of the day most drugs—probably more than half of the drugs now were discovered using AI. And 99.5% of all drug spend happens after you have a lead candidate. AI has an impact, but not nearly as much of an impact at that point compared to the early discovery. You’ve still got to put a drug in a human and test it, and that’s not something AI can accelerate. I think AI’s impact on biotech will be certainly felt, but not nearly as much as, obviously, an AI company that’s been transformational.

As it relates to Anthropic, I don’t think that people are in biotech are really focused on that, to be honest. If anything, once Anthropic and OpenAI get public, that will be a lot of capital unlocked at LPs [limited partnerships] that could potentially recycle that into other funds. If anything, I think it’s going to be a huge boon to tech and biotech to have huge values  of money, or quantums of capital, being recycled in the ecosystem.

GEN Edge: Is that because Anthropic has made moves in recent months to position itself as a life-sci research and development company?

Meyerson: Yes, they’re going to dabble. But at the end of the day, it’s just like Google, when they started playing life sciences. They’ve got to either be selling tools to those that are developing, or they’ve got to be developing [drugs] themselves. There’s no way around it. You’re doing one or the other. If they’re just selling technology to other people, that’s fine, that’s a SaaS [software-as-a-service] model. If they’re actually developing drugs and putting molecules in patients, they’re not going to have an advantage from an AI perspective. Maybe they can recruit patients a little faster, maybe they can streamline some of the results.

But the reality is, you still need to wait for disease course to play out, and you have to have patients and doctors, and all these things that they have zero experience with. So, they’re going to have to build a pharma company if that’s what they really want to do. I’m not convinced Anthropic or any of these AI companies are going to build pharmas. And if they do, they’ll probably spin it off into a separate company, because it’s not going to grow nearly as fast, it’ll be a drag on their growth. My guess is they’re going to be a drug discovery engine, and they’ll license stuff out, which is fine, but that’s still relatively low value.

GEN Edge: What has been driving PIPE activity, and what companies are more likely to go that route for going public?

Meyerson: The rationale for a PIPE is manyfold. One, there’s been many more reverse mergers that have taken place as a viable normalized way of getting public. If, let’s say, there were 20 IPOs at one point this year, there were 20 reverses, which means, really, 40 companies got public, not 20, but those 20 that got public via reverse all needed a PIPE to complete that. A lot of the PIPEs are from reverse mergers. With quality investors, the aftermarket performance of PIPEs has actually also outperformed that of the IPOs. So, there’s a feeling that you can get a better deal in a PIPE than an IPO or a secondary [offering], and as a result that’s driving people to want to participate.

Then the question is, which companies would want to do a PIPE if you can get a better deal as a secondary? The answer is nobody. Why would you want to do a pipe at a lower price if you can get a better deal as a secondary offering? I think the difference is, for companies where it’s not clear how their data is going to be reacted to by the market, you can lock in the money and not take the price action risk, versus someone who is confident the market’s gonna love the data, you can probably roll the dice, release your data, and then, then do the secondary on the heels of that data release.

We’ve seen times where people came out with data that they thought would be great, and the market hated it, and the stock went down, and then they couldn’t raise capital. So, there is some certainty and some value in doing a PIPE to have the certainty of raising the money, even if it’s potentially at a lower price than you’d have been able to get if you released the data first.

GEN Edge: The report also tracked 46 venture financing deals in North America during the third quarter, down from 55 in Q2 but above the 38 of Q3 2025. The average deal size has grown to $96 million from $83 million in Q2 and $75 million in Q3 2025, with most deals concentrated in Series B, rounds. Doesn’t this confirm what startups have long been complaining about, that they find it hard to raise capital for Series A in earlier rounds?

Meyerson: Yes. I tend to differentiate between Series A, Series B, versus stage of development, because Banyan Bio is starting companies that are Phase II companies through a Series A round. I would think about it’s probably more driven by stage of development than round of financing. Raising money for discovery slash preclinical drugs, drug companies, is really hard. And most of it is going to clinical stage and beyond, which is really saying the market has changed, fundamentally, from 2020, 2021, where if you had a clinical stage drug, you were probably not gonna raise money, because everyone was focused on high-science white space platforms, really sexy iPSCs, and all the different, technologies.

I think what people are saying is that a startup needs to be incubated longer in academia and only spin it out when you have a full development candidate, or later, if not already an IND on file. One of the reasons you’re seeing a lot of NewCos out of China with clinical or late preclinical assets is, you’re able to in-license it for cheaper than what you would have done to develop it yourself on your own. I think that’s the arbitrage out of China that a lot of these investors are seeing. So, yes, it’s a tale of two cities. If you’re doing early discovery high science it’s a very tough spot in the market.

GEN Edge: Will that, in turn, compel some of these companies to try going public instead?

Meyerson: Yes, but the public market wants clinical data also. It doesn’t value preclinical companies very highly, so you can do it, but you’d better be a really great team, with great investors, and a highly promising story in a very interesting area. Ordinarily, that’s going to be very hard to do.

GEN Edge: Where are startups looking as alternatives to traditional VC, as a result?

Meyerson: Family offices have been a big ask, but they’re not stupid either. The more a family office looks like an institutional investor, the more the answer’s going to look like it. So, disease foundations or families that have family members with a particular disease is generally an interesting way of attracting capital. The challenge is, without having them raise their hands, that they have a kid, or a family member, or a parent with that disease, it’s very hard to find them.

That’s where the disease foundations often do a very nice job, and if you partner with them, that can be an interesting approach. Some of the universities are actually investing in their own spinoffs to get to a point where they can create more value. But candidly, we used to play in that space, and we don’t anymore because of the challenge of getting funding.

GEN Edge: On the VC side, biologics’ share of financing value has continued to grow. What biologics are investors drawing toward, and what indications? Is it still cancer, maybe immunology, or has recent success brought neuro on the map?

Meyerson: Cancer and immunology are always going to be the two biggest, as a general rule, but, CNS is definitely doing okay. They’re the big three. I’m kind of surprised that rare disease has not had the same receptivity that it used to have. I think people are focused on larger markets, larger opportunities, because that’s where pharma is focused. I would say probably the most value to be found is in rare disease or smaller opportunities that could still be meaningful, just not mega-blockbuster.

GEN Edge: Looking ahead, what does the fourth quarter hold? Can you offer a preliminary look at what 2027 will hold for the markets?

Meyerson: Obviously, people are on hold until the midterm elections. I think once there’s clarity around what the balance of power is going to be, I think that will help. The hope is that there’ll be some bipartisan legislation aimed at improving the efficiency of the American drug development system. That’s something that I’m certainly supportive of and a champion of.

I’m not someone who’s going to tear down China to build up the United States. I think the United States should compete with China; I think that’s the right approach. I think that there will be, hopefully, some optimism to get some stuff done in ‘27. If that happens, I think the markets will continue apace.

It’s hard to tell: Does the interest rate continue to go up? If it goes up, I think we’re going to see a more challenging biotech ecosystem. Pharma has not gotten out from their patent cliffs. In fact, that’s a feature, not a bug. Every pharma, by definition, has a patent cliff they have to go through. The question is when and how much. So, there’s a built-in buying mechanism.

China’s not going away. There’s risk there with regulation on both sides, but fundamentally, I’m an optimist. I’m bullish on the industry, mid- to long-term. It’s very hard to predict one quarter to the next, for sure. But I don’t think the cycle has already played out. We only really started in earnest doing well a year ago. So, after four years of painful time, I don’t believe that we’re only going to have one year of a window, and then it shuts down again. I think the industry’s too large and too robust for that to happen.

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The team’s implantable device uses electrocatalysis to remove excess glucose from the bloodstream on demand. Tested in a diabesity mouse model, the device was coupled to a continuous glucose monitor (CGM) via a smartphone, enabling autonomous closed-loop metabolic control, such that the electrocatalyst was only activated when glucose levels exceeded a predefined threshold. The results showed that the device rapidly restored elevated blood glucose level to the normal range, with repeated treatments maintaining glucose control for weeks, without any drug use. Lowering glucose also shifted whole-body metabolism toward the use of stored fat, so treated mice also demonstrated reduced body weight.

Research lead Martin Fussenegger, PhD, a professor at the Department of Biosystems Science and Engineering, ETH Zürich, is senior and corresponding author of the team’s published paper in Science Advances, titled “Wireless electrocatalytic closed-loop system for drug-free management of diabesity.” In their report the team wrote in summary, “We believe that closed-loop electrocatalytic devices could establish a platform concept for regulating disease-relevant metabolites, offering a potential route toward drug-sparing, dynamically controlled metabolic interventions.”

Obesity and diabetes are closely interconnected metabolic disorders that reinforce one another through complex hormonal, inflammatory, and energy-regulation processes, together contributing to the global rise of diabesity. “The physiological relationship between obesity and diabetes is bidirectional and self-reinforcing, involving a complex interplay of metabolic, hormonal, and inflammatory changes,” the authors explained. Existing treatments rely heavily on long-term medication and lifestyle changes, while current glucose-monitoring and insulin-delivery systems face limitations in accuracy, reliability, personalization, and cost. “Current treatments rely largely on drugs that lower blood glucose or body weight, but these therapies can produce side effects, require lifelong administration, and may lose effectiveness over time,” the authors continued.

Emerging approaches, including engineered therapeutic cells and drugs that target both weight and blood glucose, offer promise but remain constrained by concerns of side effects, safety, and affordability. Another emerging strategy involves electrocatalysis, which uses electrode surfaces to drive chemical reactions that convert glucose into other compounds. “A subcutaneous implant containing a solid electrocatalyst that directly removes excess glucose from the bloodstream at an electrically adjustable rate could provide a dose-controllable, drug-free therapeutic alternative.”

Using electrocatalysis, Debasis Maity and colleagues developed the implantable ECHO device. The wirelessly powered device uses a platinum (Pt)-decorated gold (Au) nanoclusters embedded in a porous, conductive graphite felt (GF) matrix. The electrocatalyst converts circulating glucose into gluconate, which can be excreted through the kidneys.

Connected via smartphone to a continuous glucose monitor, ECHO automatically activates when blood sugar exceeds a predetermined threshold, enabling targeted glucose removal without the need for external insulin or medication.

In a proof-of-concept study in obese mice with type 2 diabetes, the authors showed that wireless activation of the device at low power (0.75 volts and 10 milliamperes) restored normal blood glucose levels within an hour, and shifted the body’s energy use from glucose toward fat. Over six weeks of treatment the mice experienced reductions in body weight and triglyceride levels, alongside increases in circulating free fatty acids and ketone bodies. These changes occurred without detectable episodes of low blood sugar or adverse effects on physiological function, inflammation, or behavior.

“The CGM system tracked glucose concentrations and transmitted the data to a smartphone, which processed the data and sent instructions back to the ECHO device, which in turn rapidly adjusted glucose concentrations to maintain homeostasis in response—achieving this without hypoglycemic episodes or the need for small-molecule drugs or biopharmaceuticals,” the authors wrote.

In a related perspective, William Bentley, PhD, and Gregory Payne, PhD, at the Fischell Department of Bioengineering, University of Maryland, wrote, “Existing bioelectronics devices provide notable access to the nervous and neuromuscular systems … future electronic intervention devices might help to manage diseases other than diabetes, modulate human-microbiome interactions, or maintain redox homeostasis in stress conditions, among many other applications.”

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