Tag: Biotech

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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.

The post Ultra- and Diafiltration Clear Leachables Effectively appeared first on GEN – Genetic Engineering and Biotechnology News.

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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.

The post Biopharma Adopting AI Despite Remaining GMP Compliance Questions appeared first on GEN – Genetic Engineering and Biotechnology News.

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Novel Targets for Complex Cancer Revealed by Genetic Regulatory Node Mapping

In a new study published in Nature titled, “Mapping convergent regulators of melanoma drug resistance by PerturbFate,” researchers from The Rockefeller University have developed a platform called PerturbFate that can systematically map how diverse disease-associated genetic variations reshape cells. By tracking gene regulation in single cells over time, the team identified regulatory nodes common to diverse variations. Using melanoma drug resistance as a proof-of-concept, results showed that these shared points of control offer a path toward combination therapies that can target disease across many genetic causes. 

“Once you know that a disease is associated with hundreds of genes, how do you design one therapy to target it?” posed Junyue Cao, PhD, head of the Laboratory of Single-Cell Genomics and Population Dynamics at Rockefeller. “We wondered whether all these different genes may be mediated by some shared downstream signaling that we can discover and target instead.” 

Advances in genomic sequencing and genetic screening have allowed researchers to identify hundreds of genetic mutations linked to disease. Yet these genes often span diverse pathways with broad functionalities, from gene regulation to cell signaling, making them difficult to target collectively.  

Cao proposed that if these mutations converge on shared downstream programs, the key challenge is not to target each mutation individually, but to identify the common control points known as regulatory nodes.  

PerturbFate allows researchers to observe how different genetic changes reshape a cell in real time by tracking DNA accessibility, and RNA production and processing. By capturing these changes in the same single cell, the system reveals the networks of genes that control cell behavior and how different genetic variations can have the same effect. 

“This technology lets us perturb hundreds to thousands of genes in parallel and then measure the detailed molecular changes in each individual cell,” says Cao. “That allows us to link many different genetic perturbations to their downstream effects and identify regulatory nodes.” 

To test the platform, the authors focused on melanoma drug resistance. Using PerturbFate, they selected 143 genes linked to resistance to the common melanoma drug, Vemurafenib. PerturbFate then tracked how deactivating each of these genes reshaped the cell. Cao explains the platform captures gene expression, RNA dynamics and chromatin state, all critical components when identifying upstream regulators that drive these disease states. 

After analyzing more than 300,000 cells, the researchers found that diverse genetic perturbations pushed melanoma cells into the same drug-resistant state. Drug resistance dropped significantly when these common control points were targeted, pointing to a promising strategy for combination therapies. 

The platform also revealed an important nuance involving the transcriptional coactivator, Mediator Complex. Disrupting different parts of this same complex could trigger drug resistance through routes that ultimately converged on the same survival signal in melanoma cells, called VEGFC. Resistant cells could no longer proliferate after blocking that signal. 

The team has made both the experimental and computational tools behind PerturbFate openly available, and plans to extend the approach from cultured cells to living systems. Cao and colleagues are currently applying PerturbFate to conditions, such as aging and Alzheimer’s disease, to uncover shared vulnerabilities that can guide more effective treatments. 

“This is just a starting point,” says Cao. “Now that we’ve demonstrated the approach in a simple model, we’re working to extend it into living systems to study even more complex diseases.” 

The post Novel Targets for Complex Cancer Revealed by Genetic Regulatory Node Mapping appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Novartis CEO joins Anthropic’s board

Want to stay on top of the science and politics driving biotech today? Sign up to get our biotech newsletter in your inbox.

Good morning. A reminder that if you’re ever feeling down about a mistake you made, there is always a way to turn it around — like how this delivery robot company has turned the issue of its robot crashing into my local bus stop into a marketing opportunity.

The need-to-know this morning

  • Revolution Medicines raised $2 billion in concurrent stock and debt offerings. The mammoth financings — double what the company intended to raise — come just days after daraxonrasib, its experimental treatment for advanced pancreatic cancer, was shown to double the median overall survival of patients in a Phase 3 clinical trial.

Bain Capital again creates a startup with older pharma drugs

After Bain Capital last summer said it licensed five immunology drugs from Bristol Myers Squibb, it’s now unveiling the company to take those treatments forward: a startup called Beeline Medicines.

Continue to STAT+ to read the full story…

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STAT+: New Bain biotech startup, building on BMS drugs, gets a name and a CEO

Last summer, Bain Capital Life Sciences announced that it would form a new biotech startup, created with $300 million and five drugs licensed from Bristol Myers Squibb.

Now that company has a name, a CEO, and a mission.

The venture, Beeline Medicines, will continue development of five inflammatory and immune disorder drugs, starting with a potential daily pill for lupus. Beeline plans to report data from a Phase 2 trial of that drug later this year. 

Continue to STAT+ to read the full story…

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Engineered Miniature CRISPR Boosts Gene‑Editing Efficiency in Human Cells

One of the biggest obstacles in targeting CRISPR therapy deliveries directly into the body isn’t the editing chemistry, it’s the size of the editors themselves. The field’s workhorse nucleases, including Cas9 and Cas12a, are considerably large (exceeding 1,300 amino acids) to fit inside adeno‑associated virus (AAV) vectors, the most widely used delivery vehicle for in vivo gene therapy. That size mismatch has forced most clinical applications to rely on ex vivo editing of blood or bone‑marrow‑derived cells, leaving many tissues out of reach. A smaller CRISPR system that can be packaged into AAV without sacrificing efficiency has long been a key missing piece.

A new study published in Nature Structural & Molecular Biology takes a major step toward that goal. Researchers at the University of Texas at Austin and collaborators report the discovery and engineering of a compact Cas12f nuclease that performs robustly in human cells, a notable advance for a class of miniature enzymes that have historically shown lower efficiencies in mammalian cells compared to larger systems. The paper is titled, “Comparative characterization of Cas12f orthologs reveals mechanistic features underlying enhanced genome editing efficiency.”

The team began by mining metagenomic datasets for naturally small CRISPR enzymes and identified a previously uncharacterized ortholog, Alistipes sp. Cas12f (Al3Cas12f). Despite its compact size—roughly one‑third that of Cas9—the nuclease showed unexpectedly strong activity in human cells. In initial screens, Al3Cas12f produced more than 50% editing at many genomic sites and exceeded 90% at several targets. The authors wrote, “Results from a gRNA screen targeting intron 1 of the ALB gene, exon 3 of the APOA1 gene and the AAVS1 site within PPP1R12C intron 1 showed that 27 target sites displayed >10% editing, 19 sites displayed >50% editing and 10 sites displayed >90% editing across AAVS1 and APOA1.”

Cryo‑EM structures revealed why this miniature enzyme punches above its weight. Compared with other Cas12f orthologs, Al3Cas12f forms a more extensive and interlocking dimer interface, creating a stable, preassembled complex that supports efficient R‑loop formation. The guide RNA scaffold also appears naturally streamlined: unlike other Cas12f gRNAs, it lacks an extraneous stem‑loop and adopts a compact conformation that docks cleanly into the protein. As the authors noted, Al3Cas12f achieves “efficient R‑loop formation through a stable dimer interface and a naturally optimized gRNA.”

Using these structural insights, the team engineered an enhanced variant, Al3Cas12f RKK, that dramatically boosts editing efficiency across genomic loci. In human cells, the variant increased editing from below 10% to more than 80% at many targets, with some sites reaching 90%. The researchers tested the system in a leukemia‑derived human cell line, focusing on genes implicated in cancer, atherosclerosis, and ALS.

The mechanistic comparisons were equally revealing. By solving the structures of two additional Cas12f orthologs—Oscillibacter sp. Cas12f and Ruminiclostridium herbifermentans Cas12f—the team noted “divergent architectures and regulatory features governing protospacer-adjacent motif recognition, gRNA binding, dimerization, and DNA cleavage.” Al3Cas12f’s extended helices and mortise‑and‑tenon‑like interactions appear to be lineage‑specific adaptations that stabilize the nuclease and support high activity.

The next step is to test whether the enzyme maintains its performance when packaged into AAV vectors. If successful, the system could offer a blueprint for engineering future generations of compact CRISPR tools.

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