Tag: Biotech

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STAT+: Researchers behind GLP-1 obesity drugs advance new approach: Drop GLP-1 as a target

The scientists whose work spurred the development of powerful obesity drugs like Eli Lilly’s Zepbound are now raising a provocative hypothesis: Perhaps targeting the GLP-1 hormone is actually not necessary to achieve effective weight loss.

A group of researchers led by Richard DiMarchi and Matthias Tschöp has created an experimental drug that activates receptors of the GIP and glucagon hormones. They propose — based on rodent and monkey studies — that this kind of molecule, when administered at high enough doses, may result in weight loss comparable to the weight loss seen with drugs that include GLP-1 as a target, and without the tolerability issues like nausea and vomiting that often come with the approved treatments, according to a peer-reviewed draft paper published this week.

The research, funded by a biotech called BlueWater Biosciences, would still need to be confirmed in humans; oftentimes results seen in animals don’t translate in the clinic. But the proposed approach, outlined in the journal Molecular Metabolism by some of the most well-known scientists in the field, is likely to stir controversy, as it challenges a central notion underpinning not just the development of approved obesity products but also next-generation versions. 

Continue to STAT+ to read the full story…

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Landmark Pancreatic Cancer Trial Highlights Promise of RAS-Targeting Daraxonrasib

Earlier this week, Revolution Medicines reported positive results from a global Phase III trial of its RAS‑targeting inhibitor daraxonrasib (RMC-6236) in metastatic pancreatic ductal adenocarcinoma (PDAC). In the RASolute 302 trial, patients receiving daraxonrasib achieved longer progression‑free survival (PFS) and overall survival (OS) than those on standard cytotoxic chemotherapy.

The RASolute 302 trial enrolled patients with pancreatic tumors harboring a wide range of RAS variants, including those with RAS G12 mutations (such as G12D, G12V, and G12R), as well as those without an identified RAS mutation. The primary endpoints of the trial were PFS and OS in patients with tumors harboring RAS G12 mutations. Secondary endpoints assessed PFS and OS in all enrolled patients (the intent-to-treat population), including those with tumors with and without (wild type) an identified RAS mutation.

Daraxonrasib patients achieved a median OS of 13.2 months versus 6.7 months for chemotherapy. The drug was generally well tolerated, with a manageable safety profile and with no new safety signals.

“With these unprecedented results, daraxonrasib has the potential to achieve our goal of bending the mortality curve in pancreatic cancer. Unlike chemotherapy, daraxonrasib is a RAS-targeted medicine that targets RAS in its active ‘ON’ state, shutting down a key signaling pathway that drives aggressive tumor growth. This is especially important in pancreatic cancer, which is among the most RAS-driven cancers, with more than 90% of tumors harboring a RAS mutation that is the driver of the cancer,” asserted Mark A. Goldsmith, MD, PhD, CEO and chairman of Revolution Medicines.

Pancreatic cancer carries one of the highest mortality rates of any solid tumor, a consequence of late-stage diagnosis and resistance to standard chemotherapy. In the United States, recent estimates point to roughly 60,000 new cases and nearly 50,000 deaths each year. With most PDAC tumors driven by RAS alterations, the early success of emerging RAS‑targeted strategies hints at how much more may be possible as this therapeutic space continues to expand.

RAS is the key oncogenic driver of pancreatic cancer. Nearly all RAS mutations occur at KRAS position G12, but RAS mutations in other isoforms and at KRAS positions G13 and Q61 are also observed. Daraxonrasib works by suppressing RAS signaling through inhibition of the interaction between both wild-type and mutant RAS(ON) proteins and their downstream effectors.

Pancreatic cancer is the most RAS-addicted of all major cancers, with more than 90% of patients harboring tumors driven by mutations in RAS proteins. These mutations span a range of RAS variants that fuel aggressive tumor behavior. Daraxonrasib, a multi-selective inhibitor of RAS(ON) proteins, is the first investigational agent in a novel class of RAS inhibitors designed to address a diverse and broad spectrum of oncogenic RAS drivers.

“For patients with metastatic pancreatic cancer, new treatment options are urgently needed to increase survival time and improve quality of life,” said Brian M. Wolpin, MD, MPH, professor of medicine at Harvard Medical School, director of the Hale Family Center for Pancreatic Cancer Research at Dana-Farber Cancer Institute, and principal investigator for the RASolute 302 trial. “The widely anticipated results of this study indicate that daraxonrasib provides a clear and highly meaningful step forward for patients with pancreatic cancer who have experienced progression on prior treatment, typically chemotherapy. I believe that this new approach is a very important advance for the field that I expect will be practice-changing for physicians and improve the care for patients with previously treated metastatic pancreatic cancer.”

Revolution Medicines now intends to submit the drug for approval by regulatory authorities, including the U.S. Food and Drug Administration as part of a future New Drug Application, and for presentation at the 2026 American Society of Clinical Oncology Annual Meeting. 

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An inducible base editing platform for cancer functional genomics in vivo

Nature Biotechnology, Published online: 15 April 2026; doi:10.1038/s41587-026-03079-3

We developed a functional genomics platform using a small-molecule-controllable base editor that enables gene editing with reduced cellular toxicity and minimal transcriptional perturbation. The resulting high efficiency of the method potentiates in vivo inducible genetic screening, allowing systematic identification of critical residues in cancer therapeutic targets.

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Fujifilm Biotechnologies Opens New QC Lab in Denmark

Fujifilm Biotechnologies, a CDMO, celebrated the opening of its new, 2,000‑square‑meter quality control (QC) laboratory at its Hillerød, Denmark, commercial‑scale manufacturing site. The expanded QC footprint will enable bioassay and virology operations and support the site’s planned expansion, according to the company.

The laboratory features ventilation systems, personnel, and material airlocks, and an open‑plan layout. The space supports approximately 100 quality team members to conduct viral safety testing for drug substance/product release, scale capacity for complex cell‑based potency and ELISA methods, and perform raw material and critical total organic carbon cleanability studies to accelerate future partner campaigns.

The QC laboratory also incorporates robotics and an ongoing LIMS implementation across the company’s global network of sites to enable digital harmonization and data integrity.

The company doubled its Hillerød capacity in 2024 from six to 12 x 20,000 L mammalian cell culture bioreactors, increasing the complexity and volume for QC testing. The expanded production scale required expanded QC capabilities and advanced analytical equipment to support current operations and anticipated future demand. The QC lab is housed within a new 7,600-square-meter building that also features employee amenities, office and collaboration space, utility services, and an emergency generator to ensure uninterrupted operations and timely delivery of critical test results.

Construction of the lab was completed in last month and subsequently received approval from the Danish Medicines Agency (DKMA) following an on‑site inspection. Laboratory operations will begin in May 2026.

The new QC laboratory is part of Fujifilm Biotechnologies’ kojoX modular, connected network of manufacturing facilities, where harmonized equipment, layouts, methods, and digital systems are used to enable cross‑site workflows and consistent application of quality standards across regions, explains Christian Houborg, senior vice president and Hillerød site lead.

“Today, we are opening a world-class GMP-approved QC laboratory to elevate our quality control and be ready for the upcoming expansion, thereby continuing to manufacture advanced biological treatments for patients with severe diseases, such as cancer and rare autoimmune diseases. Together, we’re making a measurable impact for patients and partners around the world,” he said.

 

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Digital Twin Process Could Slash Microbial Protein Costs

A consortium of companies has developed what they call a digital twin of a microbial process to produce protein A.

Novasign, based in Vienna, hopes its participation through the ECOnti consortium will help manufacturers slash the costs of microbial proteins by improving experimental design.

According to the company, the digital twin can reduce the number of experiments needed to understand process behavior by 70% compared to Design of Experiments (DoE).

“Generally, the biggest problem in the industry right now is it’s not very efficient,” explains Mark Duerkop, PhD, CEO of Novasign.

“We need methods to learn more efficiently from experiments, design better experiments, and adapt process trajectories if something goes wrong.”

According to Duerkop, Novasign began developing an end-to-end digital twin of the full processing chain for microbial protein production as part of the ECOnti consortium three years ago.

Novasign says it develops digital twins spanning an entire process—from upstream to downstream—with the goal of improving both process development and manufacturing efficiency.

“The digital twin supports process development by systematically recommending the next set of experiments based on model-informed insights,” he says.

Setup of the Novasign ECOnti Digital Twin Technology

“During manufacturing, it can detect deviations from the intended process trajectory and support corrective actions.”

For example, if the digital twin is used to recover a process following disturbances, such as pH shifts or feed pump failure, manufacturers could significantly reduce product losses.

However, this remains for the future, he says, as the U.S. Food and Drug Administration (FDA) requires extensive validation before approving self-optimizing or autonomous manufacturing processes.

At the recent Bioprocessing Summit Europe, Duerkop presented a showcase on using the Novasign Studio software for full process control for 30 consecutive days.

He also showed how the software can use small-scale experimental data to inform scale-up and, in biosimilar development and viral vector manufacturing, can reduce experimental effort by up to 64%.

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Ultra- and Diafiltration Clear Leachables Effectively

In the push to de-risk biologics manufacturing, downstream purification steps are increasingly under the microscope. Now, new research led by Jonathan Bones, PhD, principal investigator in the characterization and comparability group at the National Institute for Bioprocessing Research in Dublin, and his colleagues provided compelling evidence that ultrafiltration and diafiltration (UF/DF) deliver robust clearance of process-related leachables—while also offering a predictive framework to better understand that performance.

Although UF/DF has long been assumed to reduce small-molecule contaminants, systematic data have been scarce. To address this gap, the team evaluated 28 representative organic compounds spiked into three distinct protein systems. Using liquid chromatography–high resolution mass spectrometry, they tracked how effectively these compounds were removed during UF/DF operations.

The results were striking. Twenty-four of the compounds demonstrated greater than 98% clearance across all three protein processes. Notably, variations in protein characteristics and process parameters had minimal impact on removal efficiency. Instead, clearance behavior was remarkably consistent, as reflected in similar sieving coefficients across the systems.

The intrinsic physicochemical properties of the leachables impacted clearance. Among these, lipophilicity—expressed as the octanol-water partition coefficient (Log P)—emerged as the dominant factor. Compounds with Log P values below four exhibited near-ideal clearance, while even highly hydrophobic molecules (Log P above seven) still achieved removal rates exceeding 93%. Molecular weight, polarizability, and solvent-accessible surface area also contributed to clearance outcomes.

Beyond empirical findings, the study advances the field with predictive modeling. By applying orthogonal partial least squares (OPLS) regression, the researchers developed tools capable of estimating sieving coefficients based on compound properties. These models could prove invaluable for anticipating leachable behavior without exhaustive experimental testing.

The implications are significant. As regulatory scrutiny around extractables and leachables intensifies, demonstrating effective clearance becomes central to product safety. This work not only confirms that UF/DF is a powerful mitigation step but also equips developers with quantitative tools to support risk assessments.

In an industry where unseen contaminants can pose outsized risks, the ability to both measure and predict their removal marks a meaningful step forward.

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iPSC-based Manufacture vs. Autologous Model Production Costs Examined via Financial Analysis

Autologous and allogeneic cell therapies are establishing viable clinical pathways but cannot be manufactured cost-effectively at scale. Manufacturing natural killer (NK) cell therapies, and possibly T-cell therapies, using induced pluripotent stem cells (iPSCs) is understood to be significantly more cost-effective. Now those cost advantages have been quantified.

Specifically, the cost of goods per treatment can be reduced as much as 95% when manufacturing via iPSCs rather than using traditional autologous or allogenic production methods. By decoupling production from the patient, manufacturers can benefit from large-scale batch production, standardized processes, less labor, and a less complex infrastructure than either autologous or allogeneic production. Details are spelled out in a white paper by Cellistic, based on an intense cost-of-goods analysis of NK cell therapy manufacturing performed by Astrid Van Damme, PhD, head of project management at Cellistic, for her MBA thesis.

In it, Van Damme advocates creating a universal master cell bank that feeds multiple working cell banks. Those working cell banks, in turn, generate intermediate hematopoietic stem cells that are differentiated into the final therapeutic product. “This cascade creates an essentially inexhaustible, standardized source material for the entire commercial lifecycle of the product,” she asserts.

Cellistic’s internal review compared seven economic drivers for each of the three cell therapy manufacturing options. Notable advantages for an iPSC manufacturing strategy include:

  • Commercial scale production
  • Exponential scale-up or scale-out
  • Industrial-scale reproducibility
  • Use of standard cold-chain logistics
  • Minimal patient interactions
  • Reduced patient attrition
  • Potentially global market reach

An iPSC manufacturing strategy for cell therapies drops the cost of goods sold to about $5,000 per dose, down from $115,000 per dose for autologous therapeutics and $40,000 per dose for allogeneic therapeutics, Van Damme reports.

Autologous and allogeneic manufacturing, in contrast, both have severe constraints that increase costs for manufacturers and payers alike. Materials and labor alone account for 50% to 70% of autologous cell therapies—roughly $80,000 to $150,000. That’s a huge driver for U.S. list prices that, for the oncology therapeutics Kymriah® and Carvykti®, are at or above an adoption-limiting $475,000 per dose. Even after factoring in regional pricing differences and payer discounts, the net per-dose costs to payers are still extremely high.

Compared to iPSC manufacturing at clinical scale (150 vials and 200 M cells per vial) and at commercial scale (450 vials with 400 M cells per vial), Van Damme indicates:

  • Labor costs constituted about 13% of the costs of goods (vs. about 70% for autologous methods)
  • Costs per vial drop approximately 40%
  • Fixed costs were diluted by a factor of three

“Once a minimum threshold of operational maturity and throughput is achieved, iPSC-based manufacturing economics become comparatively robust to routine operational variability,” the paper concludes.

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Biopharma Adopting AI Despite Remaining GMP Compliance Questions

The biopharma industry is embracing artificial intelligence (AI) in manufacturing, even though questions remain about how best to use the technology in a GMP environment.

At least, so says Sanjay Konagurthu, PhD, senior director, science and innovation, pharma services, at Thermo Fisher Scientific, who argues that the key to successful AI adoption is a clear use case.

“As is true in most industries, adoption of AI and machine learning (ML) is real and accelerating across the biopharma industry. However, most use cases center around applications that augment teams without completely redefining a validated process, such as smarter quality and inspection workflows.

“We’re seeing that the hesitation isn’t so much reluctance to adopt AI as it is the practical constraints of operating in a good manufacturing practices-regulated environment. To adopt AI and ML in regulated environments, you need clear intended use, strong data foundations, traceable governance, and set parameters for disciplined control and monitoring,” he tells GEN.

Data foundations

In addition to establishing a strong use case, drug companies need an IT infrastructure that facilitates the flow of process data, according to Konagurthu, who says data silos are a persistent problem in biopharma.

“As with most scientific endeavors, vast quantities of data are generated across the biopharma industry, among labs, organizations, consortia, and nations, and much of this data is stored in a singular system. So, there’s a connectivity challenge, but that’s not the only reason why processing technologies struggle to exchange information.

“The data is often captured according to different standards and exists in a variety of formats, which means context is easily lost. Also, the data exists in a wide variety of structured and unstructured formats, which compounds the challenge in effective curation and analysis,” he says.

Failure to establish an effective infrastructure or standardize data has multiple negative consequences, Konagurthu adds.

“When data from early development can’t be connected through to commercialization, teams end up re-running experiments and analysis. They may even miss early signals that could impact downstream manufacturing or risk quality.

“When companies look to scale or implement new technologies like AI and ML, fragmented data can become prohibitive. Ultimately, for biopharma, this could extend the time it takes to bring a promising molecule to market,” he says.

Formul-AI-tion development

Beyond process development and control, formulation is another area where more and more biopharmaceutical companies are making a use case for AI.

Konagurthu says, “Biopharma scientists have historically used trial-and-error approaches to determine the right solubility and bioavailability of OSD [oral solid dose] therapies. With AI and ML models, teams can make earlier, better-informed decisions on formulation pathways.

“Early-stage acceleration in discovery and formulation echoes all the way into manufacturing and clinical supply, so improving the front end can compress timelines across the entire pipeline,” he adds.

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