Tag: Biotech

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Forecasting Protein Aggregation with an Improved Algorithm

A new, improved algorithm for studying protein aggregation could help biologics manufacturers design better-performing products with less experimental effort. The software, developed by scientists based in Barcelona, offers the ability to analyze the aggregation of proteins drawn from the AlphaFold protein structure database, as well as helping companies identify more soluble alternatives.

“Protein aggregation is a bottleneck in the production and manufacturing of biologics,” explains Salvador Ventura, PhD, a professor in the department of biochemistry and molecular biology at the Autonomous University of Barcelona (UAB).

The problem, he explains, is that many proteins used as therapies evolved to be soluble at the concentrations found in the human body. But therapeutics, such as antibodies, are produced in as high a concentration as possible.

“We want the product to deliver the maximum dose with the minimum amount of injection,” he says. “But proteins aren’t designed to be soluble at these concentrations, and their aggregation causes different effects.”

These can include the patient’s immune system reacting negatively or the aggregated product ceasing to work.

To overcome this problem, Ventura says, companies and labs try to forecast protein aggregation, usually experimentally with high-throughput combinational assays. But these approaches are not convenient for startups or small spinoff companies.

A computational approach, such as his algorithm, now in its fourth generation, can help these companies predict and design around protein aggregation.

It offers the ability to draw protein structures from AlphaFold to analyze likely protein aggregation using simulations of molecular dynamics. Users, he says, can also choose to mutate selected parts of the protein, identify other proteins in the same family, and even look at the possible impact of pH on solubility.

“Our lab is both computational and experimental, so most of the designs we’ve made, we’ve already proved by experiment,” Ventura says.

Limitations include the scarcity of high-quality experimental data available to train the algorithm, he explains.

Going forward, the team intends to model which solution and formulation conditions best maintain the stability of therapeutic proteins in manufacturing and clinical settings. “We’re working on these next steps already,” he says. “Although, as yet, we don’t have an algorithm for this.”

Ventura spoke about the latest version of his algorithm at the Bioprocessing Summit Europe in March.

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Faster Process Development via “Transfer Learning”

An emerging artificial intelligence technique called “transfer learning” could help drug makers use data to speed up the development of biopharmaceutical manufacturing processes, according to new analysis.

In transfer learning, predictive models that have been trained on historical data are used to improve the performance of a task.

Unlike machine learning (ML)—where the training process begins from scratch—transfer learning applies existing knowledge to new but related problems, reducing the amount of data and time required to build the model.

Researchers at the Karlsruhe Institute of Technology in Germany, who looked at the approach, identified several potential biopharma applications, according to lead author Daniel Barón Díaz, citing reactor modeling as an example.

“Transfer learning models can be used to predict critical outcomes like viable cell density (VCD) and product titre from online sensor data—for example, pH, temperature, gas flow—from historical data from a different, but related process.”

The approach can also optimize process monitoring. Díaz tells GEN that, “Transfer learning-enhanced soft sensors can be established to monitor protein concentrations in real-time by leveraging existing models from related fermentations.”

Data limitation

When compared with other model-building techniques, transfer learning offers potential cost and time savings, according to Díaz, who cites a reduced experimentation burden as an example.

“Conventional machine learning requires large, structured datasets that are often unavailable in biopharma due to the high cost and labor-intensive nature of experiments. Transfer learning allows companies to leverage historical data and existing models to build reliable predictors for new processes with very limited data.

“By reusing prior knowledge, transfer learning can significantly decrease the number of experiments required—sometimes needing only one to three batches to achieve robust simulations,” he says.

However, the ultimate benefit is that transfer learning speeds up process model development, according to Díaz, who adds, “It can make model adaptation faster than retraining from scratch, facilitating quicker process design and digital twin deployment.”

Challenges

So, transfer learning has the potential to create predictive models for manufacturing development. However, the key caveat is that the processes involved must be sufficiently similar for it to be effective, Díaz says.

“For transfer learning to be effective, the source and target domains must be meaningfully related. If the processes are too different, the assumptions and learned representations may not align, leading to negative transfer, where the transferred knowledge actually degrades the model’s performance.

“Data sets obtained at different scales or under varying conditions are often inconsistent, which can hinder the successful transfer of knowledge. Fine-tuning complex neural network architectures on very small target datasets can lead to overfitting, where the model fails to generalize to new data,” he says.

To address this, manufacturers will need to establish metrics to determine similarity, Díaz explains.

“There are currently no standardized metrics for measuring domain similarity in bioprocessing, nor are there comprehensive benchmark datasets to easily compare different transfer learning techniques.”

Another challenge is the current lack of AI expertise in the industry, Díaz says.

“There is often a disciplinary knowledge gap between process engineers and data scientists, and ML models without a mechanistic backbone may be perceived as opaque black boxes, hindering trust and industrial adoption,” he tells GEN.

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Redefining Bioprocessing Using Reservoirs of Biochemical Diversity

In the global race to improve how medicines are made, scientists are turning to an unlikely source of innovation: the microscopic life thriving in some of the harshest soils on Earth. Beneath wild plants in Saudi Arabia’s arid landscapes, researchers have identified biological tools that could redefine bioprocessing.

A recent study by Saudi Arabia-based Rewaa S. Jalal, PhD, associate professor of biology at the University of Jeddah, and Fatimah M. Alshehrei, PhD, associate professor of microbiology at Umm al-Qura University, focuses on the rhizosphere—the thin layer of soil surrounding plant roots—where dense microbial communities interact with their host plants. These environments, shaped by extreme heat and limited water, are proving to be reservoirs of biochemical diversity with direct relevance to drug manufacturing.

The researchers zeroed in on enzymes known as glycosyltransferases, which play a central role in building complex sugar structures on proteins and other molecules. In pharmaceutical bioprocessing, this step—glycosylation—is crucial. It determines how therapeutic proteins behave, influencing everything from stability to effectiveness and immune compatibility.

What makes these enzymes especially compelling is their environmental pedigree. The microbes that produce them have adapted to survive under intense stress, evolving systems that remain functional in high temperatures and low-moisture conditions. These traits could translate into more robust and flexible bioprocessing workflows, where maintaining strict environmental control is often costly and technically demanding.

The study also reveals that different plant species cultivate distinct microbial communities, each enriched with unique enzyme families. For example, the rhizosphere of Moringa oleifera shows a different enzymatic profile compared to Abutilon fruticosum, highlighting how plant-microbe partnerships shape biochemical potential. For bioprocessing, this diversity could enable the selection of highly specific enzymes tailored to particular drug production needs.

Beyond protein modification, the identified enzymes are linked to the synthesis of key biomolecules such as cellulose, chitin, and β-glucans. These materials are already used in areas like drug delivery, wound care, and tissue engineering. Improving how they are produced through advanced bioprocessing could expand their applications and reduce manufacturing constraints.

Despite the promise, the researchers emphasize that their findings are based on computational analysis of genetic data. The real-world performance of these enzymes in industrial bioprocessing systems remains to be tested.

Still, the implications are significant. As pharmaceutical companies seek more sustainable and efficient ways to produce complex biologics, enzymes shaped by extreme environments might offer a powerful advantage. Instead of engineering solutions from scratch, scientists are increasingly uncovering them in nature—already optimized through evolution.

In this emerging vision of bioprocessing, the future of medicine might be shaped not only by cutting-edge technology but also by the resilient microbial ecosystems hidden beneath desert plants.

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Plant Molecular Farming Comes of Age

Plant molecular farming (PMF) may seem like a bold option for companies accustomed to mammalian or microbial systems, but recent advances have transformed plant-based bioproduction into serious, scalable biomanufacturing platforms able to produce even complex biologics cost-effectively.

“A major advantage is sustainability,” Marco P.C. Marques, PhD, associate professor, University College London (UCL), tells GEN. This comes at a time when “…regulators and global initiatives are putting real pressure on industry to reduce environmental footprint(s). Because plants grow using low energy inputs rather than stainless steel reactors or energy-intensive systems, they can bring down operating costs, reduce carbon emissions, and provide more flexible manufacturing options.”

Additional benefits include PMF systems’ ability to support eukaryotic protein-folding and post-translational modification pathways, their lack of human pathogens, minimal biosafety risks, and compatibility with distributed manufacturing.

PMF reached its current state because sensors, host plant engineering, AI-enabled models, and related technologies have become more mature, reliable, and predictable in the past few years. Consequently, “PMF platforms can deliver consistent, good manufacturing practice (GMP)-compatible performance while needing far less infrastructure, [which] allows much faster setup than conventional approaches,” Marques says.

Robust, economic, responsible

In a recent paper, he and first author Teresa Iucci, PhD, a bioprocessing scientist at Sapienza University of Rome and UCL, cite 13 companies that are using or have used plants to produce a variety of proteins, including antibodies, enzymes, and peptides, for vaccines and other biologics. Many are at clinical or commercial scale.

Those examples show “that controlled cultivation, advanced transient-expression systems, and more refined downstream workflows can overcome many of the technical and regulatory hurdles historically associated with plant-based biomanufacturing.” In particular, they note substantial improvements in host plant engineering. Now, they point out, Nicotina plants can produce mAbs and Fc-fusion proteins that closely match those derived from CHO cells.

However, “Realizing the full value of these biological innovations will depend on aligning PMF with contemporary digital manufacturing principles,” Iucci and Marques stress.

“There is a lot of scope for continued innovation…particularly on the molecular biology side, where further gains in expression, stability, and product quality are very achievable,” Marques elaborates. “Downstream processing could also be better tailored to plant-based hosts,” to lower costs further.

The benefits of PMF are well-recognized, but biomanufacturers also need clear, streamlined regulatory pathways and the internal determination that PMF is worth sustained investment.

For biomanufacturers, “A good starting point is simply to treat PMF as a genuine production platform rather than an interesting alternative,” he says. To be able to compare PMF products with those derived from traditional mammalian or microbial cultures, he calls for the industry to standardize unit operations and generate regulatory-grade datasets, and then to run comparability studies and pilot-scale campaigns.

Running such campaigns is becoming increasingly practical with the conjunction of sensors and data-driven processors. In vertical farming facilities, for example, every parameter critical for plant growth is tightly monitored and controlled using digital sensors to enable precise, real-time environmental adjustments.

Ultimately, this allows producers to select the optimal timing of such events as infiltration and harvest at levels not possible in conventional greenhouses. “The long-term objective is a semi-continuous, digitally regulated PMF production line that links infiltration, extraction, and purification into a coherent, self-correcting workflow,” Iucci and Marques write.

Transitioning from mammalian or microbial systems to PMF, “is easier said than done…especially when companies already have well-established mammalian or microbial platforms with validated processes and established supply chains,” Marques acknowledges. “In many respects, it would be simpler to design a PMF-based approach from scratch than to retrofit it into an existing operation…but with the right incentives (such as additional revenue streams from side processes), application cases, and evidence, we may well see more companies prepared to make that shift.”

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Childhood Dementia Explained by Synaptic Dysfunction, Opens New Therapies

In a new study published in Nature Communications titled, “Modelling synaptic dysfunction in childhood dementia using human iPSC-derived cortical networks,” researchers from Flinders University in Adelaide have uncovered how hyperactive and dysregulated synaptic circuits emerge in the brain tissue of children impacted by Sanfilippo syndrome, a common form of childhood dementia. 

In Australia, an estimated 1400 children currently live with childhood dementia, with hundreds of thousands of cases worldwide. Sanfilippo syndrome is a rare genetic condition that causes fatal brain damage. Children typically reach early developmental milestones before rapidly losing cognitive skills, speech, and mobility. Early symptoms often include hyperactivity and sleep disturbance. 

Alterations in synaptic communication play key roles in neurodegenerative disease progression and cognitive decline. Yet few studies have explored how excitation and inhibition synaptic imbalances contribute to pediatric neurodegenerative disorders. 

Cedric Bardy, PhD, professor and head of the Laboratory for Human Neurophysiology and Genetics at the South Australian Health, describes the study findings as “significant progress.” Chronic overactivity in the brain appears to be a fundamental mechanism contributing to cognitive deterioration in children with Sanfilippo syndrome. 

Using human stem cell-derived cortical neurons and electrophysiology, the team demonstrated that excitatory synapses in the neurons of affected children become abnormally active during early brain development. 

While these neurons initially developed and functioned normally, they became increasingly overactive over time. Brain cell networks showed bursts of intense, highly synchronized electrical activity as they matured, mirroring the hyperactivity and neurological symptoms seen in children with the condition. 

“This hyperactivity offers a clear biological explanation for early behavioral changes, and it brings us closer to understanding the complex mechanisms contributing to childhood dementia,” said Bardy.

Results also demonstrated that these neurons are vulnerable to stress. When exposed to mild nutrient deprivation, excitatory synaptic abnormalities increased, suggesting that common illnesses or physiological stressors may accelerate neurological decline. 

“Our research shows that disrupted synaptic communication is not simply a byproduct of degeneration. It is an early driver of the disease,” Bardy says. 

Childhood Dementia Initiative CEO and founder, Megan Maack, is a co-author of the study and has been involved in guiding the project since its inception. 

“This research is significant not just for Sanfilippo syndrome, but for the field of childhood dementia as a whole,” said Maack. “By identifying the precise cellular mechanisms driving the disease, we are moving towards a personalized medicine approach—the kind of targeted treatment strategy that has transformed outcomes for children with cancer.”

Researchers are now evaluating whether drugs that are already on the market for use in other conditions could be repurposed for childhood dementia. Bardy says the team has already demonstrated that these synaptic imbalances can be corrected with certain medications in the laboratory, indicating that they represent a genuine therapeutic target. 

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New Single‑Cell Platform Tracks RNA and Protein in Immune Signaling

A new single‑cell sequencing method is giving researchers a clearer view of how immune cells actually behave—capturing not just what they plan to do, but what they are doing in real time. The platform, called CIPHER‑seq, measures RNA and proteins simultaneously inside the same immune cell, exposing gaps between genetic intent and functional output that have long complicated studies of cytokine signaling. The work, titled “CIPHER-seq enables intracellular multimodal profiling of cytokine responses in single immune cells,” appears in Scientific Reports.

Single‑cell RNA sequencing has reshaped immunology by revealing which genes are switched on across thousands of cells at once. But RNA alone can be misleading, especially for cytokines. However, RNA is only a set of instructions; proteins carry out the action. And for cytokines, RNA levels often fail to predict how much protein a cell actually produces. “In immune cells, RNA and protein don’t always rise and fall together,” said co‑senior author Emiliano Cocco, PhD, an assistant professor of biochemistry and molecular biology at the Miller School.

CIPHER‑seq (Cytokine Intracellular Protein High-throughput Expression with RNA-sequencing) was designed to close that gap. Developed by researchers at the Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, together with collaborators at UCSF and the Helen Diller Family Comprehensive Cancer Center, the method gently preserves cells and captures multiple molecular layers at once. From a single immune cell, CIPHER‑seq can quantify genome‑wide RNA, surface proteins, intracellular proteins, and cytokines that have not yet been released—creating a more complete snapshot of immune activity than RNA‑only approaches.

“RNA gives us clues about where a cell is headed,” said co‑senior author Justin Taylor, MD, a Sylvester physician-scientist. “Proteins show us where it actually arrives, and this clearer picture could help scientists design better immunotherapies and help clinicians predict which patients are most likely to benefit from them.”

The team validated the platform by stimulating peripheral blood mononuclear cells (PMBCs) and tracking their responses. According to the study, CIPHER‑seq captured robust induction of key cytokines—including interferon‑gamma and tumor necrosis factor—while also resolving metabolic remodeling during activation. Importantly, the method revealed the timing of these events: RNA signals rose first, followed by delayed but consistent protein accumulation. First author Avni Bhalgat, PhD, described it as “seeing the plan before the action. Cytokines help determine whether immune cells attack cancer, ignore it, or even help tumors grow.”

The researchers also compared CIPHER‑seq with standard single‑cell workflows and found a notable difference: cells processed with CIPHER‑seq showed far fewer mitochondrial stress signatures. Some existing protocols inadvertently damage cells during preparation, triggering artificial stress responses. By reducing these artifacts, CIPHER‑seq provides a cleaner readout of immune behavior.

The authors emphasize that this multimodal view is especially valuable for studying cancer, inflammation, and treatment resistance—contexts where cytokine timing and protein abundance can shape therapeutic outcomes. “The platform helps us move beyond inference and toward understanding how immune responses truly unfold—one cell at a time,” Taylor added. By tracking RNA and protein together, CIPHER‑seq moves researchers beyond inference and toward a step‑by‑step understanding of how immune responses unfold.

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STAT+: A decade ago, these drugs tore apart the FDA. Today, they might be some patients’ best hope 

A year after the worst day of her life, Debra Miller received a voicemail she couldn’t quite make out. In a thick accent, a man said something about research and left a phone number. She called but couldn’t get through. “I didn’t know what country code to put in,” she said.

Debra moved on, but the voice kept tumbling through her brain. She was desperate. Her first child, Hawken, had been diagnosed 13 months before with Duchenne muscular dystrophy. In blunt tones she would never forget, a doctor had told her that her 5-year-old boy would slowly lose the ability to walk and die by 18.

When she finally figured out the digits, a Dutch scientist explained he was launching a startup around one of the most counterintuitive ideas in modern genetics: that sometimes you can fix a broken gene by breaking it just a little bit more. 

That strategy, known as exon skipping, would taunt Debra for two decades, always promising a therapy just out of reach. It prompted her to raise $1.3 million for the Dutch scientist and helped turn her fledgling advocacy group, CureDuchenne, into a powerhouse. Eventually, the idea spread far beyond the Netherlands and Debra’s home in Newport Beach, Calif., stirring tenuous hope for a life-altering treatment. 

Exon-skipping drugs sparked a civil war within the Food and Drug Administration. Under pressure from advocates and companies, a top official overrode reviewers to approve the first of several candidates. One company, Sarepta Therapeutics, has since earned over $5.5 billion from from drugs that may or may not provide much benefit. 

Throughout, by the fickle winds of scientific misfortune, mother and son remained waiting — until about two and a half years ago. That’s when Hawken enrolled in a clinical trial for a new exon-skipping drug Debra helped support. The results from him and 38 other patients have since stunned some of the field’s top experts. 

Continue to STAT+ to read the full story…

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Neurocrine Grows in Endocrinology, Rare Disease with $2.9B Soleno Buyout

Neurocrine Biosciences has agreed to acquire Soleno Therapeutics for $2.9 billion, the companies said, in a deal designed to bolster the buyer’s portfolio of marketed endocrinology and rare disease therapies.

“This transaction will advance Neurocrine’s mission to deliver life-changing treatments while accelerating our revenue growth and portfolio diversification strategy,” Kyle W. Gano, PhD, Neurocrine’s CEO, said in a statement.

The acquisition would bolster Neurocrine’s offerings to include three treatments that have already reached the market:

  • Crenessity® (crinecerfont), a treatment of classic congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency that received FDA approval in December 2024
  • Ingrezza® (valbenazine), a vesicular monoamine transmitter 2 (VMAT2) drug approved in 2017 as a treatment for tardive dyskinesia and the chorea associated with Huntington’s disease
  • Vykat™ XR (diazoxide choline), approved last year as the first and only therapy indicated to treat hyperphagia in patients ages four and older with Prader-Willi syndrome (PWS).

“Neurocrine is the right strategic partner to expand the reach of Vykat XR in the Prader-Willi syndrome community given their experience in endocrinology and rare disease and their proven ability to execute successful commercial launches,” stated Anish Bhatnagar, MD, Soleno’s chairman and CEO. “We are excited to accelerate Vykat XR’s impact for PWS patients following completion of the transaction by leveraging Neurocrine’s strong commercial capabilities.”

Soleno finished 2025 with $190.4 million in net revenue from sales of Vykat XR—including $91.7 million generated during the fourth quarter, pushing the company to profitability with positive net income of $20.9 million.

‘A little surprising’

Stifel analysts Paul Matteis and James Condulis called the planned acquisition “a little surprising” since Vykat XR is projected to garner approximately $400 million in annual net revenue, he commented in a note reported by Bloomberg News.

Vykat XR won FDA approval in March 2025. From then through December 31, 859 active patients were prescribed the drug by 630 unique prescribers (136 of them in Q4), while the company received 1,250 patient start forms (207 in Q4).

Neurocrine expects Vykat XR’s numbers to improve in coming years, since the drug is positioned as a foundational first-line therapy for PWS and is supported by a patent portfolio that is expected to protect the drug’s exclusivity into the mid-2040s.

Vykat XR would join Neurocrine’s marketed portfolio which includes Ingrezza and Crenessity. Ingrazza racked up blockbuster net revenue numbers of $2.51 billion up 9% year-over-year (including $657.5 million during Q4, up 7% from the year-ago quarter). Neurocrine has credited double-digit prescription volume growth in total prescriptions and new (first-time) prescriptions, partially offset by a lower net price that the company called new “formulary access investments” designed to support long-term growth.

Crenessity generated $301.2 million in net product sales last year for Neurocrine, including $135.3 million in the fourth quarter, reflecting 2,048 total new patient enrollment start forms, 431 of them in Q4 2025.

Neurocrine reasons that the three drugs will deliver sustained revenue growth for the combined company through the end of the decade.

Also for Neurocrine, a buyout of Soleno presents a “more sensible way into metabolic disease” than by developing its own pipeline candidates, which are in preclinical phases, and risking competitive and regulatory challenges, BMO Capital Markets analyst Evan Seigerman observed in a research note reported by Reuters.

Neurocrine has disclosed plans to begin Phase I studies this year for two preclinical obesity candidates: NBIP-‘2118, a CRF2 agonist; and ‘NBIP-‘1968, a combination of ‘2118 and the company’s own GIP (glucose-dependent insulinotropic polypeptide)/ GLP-1 (glucagon-like peptide-1) preferring triple agonist, which Neurocrine calls “light” on glucagon activity.

News of a potential buyout of Soleno by Neurocrine was first reported Sunday by the Financial Times.

Soleno investors signaled approval of the buyout Monday by sending shares to $52.25, up 32% from Thursday’s close of $39.49 (Markets were closed Friday for Good Friday). However, Neurocrine’s investors weren’t as supportive of the deal as that company’s shares barely budged, closing at $132.48, up 0.67% from $131.60 on Thursday.

Second thoughts?

A potential reason: Neurocrine investors may have second thoughts about a deal that would add to its pipeline Vykat XR, whose prescribing label includes warnings and precautions about past reports of hyperglycemia and fluid retention/edema, as Sumant Kulkarni, a senior analyst covering biotechnology with Canaccord Genuity, commented in a research note.

“We believe NBIX would have to articulate its plans very well for investors to display enthusiasm from the get-go,” Kulkarni wrote.

Yet two things could work in Neurocrine’s favor, Kulkarni added: The company’s solid track record of commercialization as seen with Ingrezza and Crenessity, and the prospect of adding to the portfolio Vykat XR given its approval for a rare form of obesity.

San Diego-based Neurocrine reported approximately 2,000 employees as of December 31, 2025, with plans during the first quarter to complete the expansion of sales teams for Ingrezza and Crenessity “to maximize our commercial momentum.” Soleno is based in Redwood City, CA, and reported a workforce of 182 full-time employees as of the end of 2025.

At $53 per share cash, the purchase price represents a premium of about 34% above Soleno’s closing share price Thursday, and a premium of 51% to Soleno’s 30-day volume-weighted average price (VWAP).

The boards of both Neurocrine and Soleno have approved the transaction, which is expected to close within 90 days subject to satisfying customary closing conditions that include receipt of regulatory approvals.

Neurocrine will acquire Soleno by launching a tender offer for that company’s outstanding shares. Following a successful completion of the tender offer, a wholly owned subsidiary of Neurocrine will merge with Soleno, and the outstanding Soleno shares not tendered in the offer will be converted into the right to receive the same $53 per share in cash paid in the tender offer.

Consummation of the tender offer is subject to the tender of at least a majority of the outstanding shares of Soleno, the expiration or termination of the waiting period under the Hart-Scott-Rodino Antitrust Improvements Act of 1976, and other customary conditions.

Neurocine said it will fund its acquisition of Soleno using a “modest amount” of pre-payable debt plus cash on hand. Neurocrine reported $1.48 billion in cash, cash equivalents, and marketable securities as of December 31, 2025—up 37.5% from $1.076 billion a year earlier.

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