Dynamic42 and EPO (Experimental Pharmacology and Oncology), both based in Germany, report that they are addressing the limited availability of preclinical models in brain cancer research by forming a strategic collaboration that focuses on bringing organ-on-chip technologies “closer to the core of preclinical drug development.”
The partnership combines Dynamic42’s organ-on-chip platforms with EPO’s expertise in translational oncology and access to well-characterized tumor models and patient-derived material. Together, the teams are developing experimental setups designed to reflect human tumor biology more closely and generate data that translates more reliably into clinical outcomes.
The first joint projects target glioblastoma and the blood–brain barrier (BBB). Using Dynamic42’s human-based BBB-on-chip model, the partners will explore how differences between human and non-human BBB-biology can influence therapeutic responses, which is a major factor for the limited activity of brain cancer drugs.
“Too often, critical decisions in drug development rely on data that do not fully reflect human biology,” said Thomas Sommermann, PhD, head of cancer research at Dynamic42. “We want to change that. By bringing human-based models earlier into the process, we can sharpen decision-making and reduce late-stage failure risks.”
“For us, this collaboration is about strengthening the translational link,” added Jens Hoffmann, CEO at EPO. “Integrating advanced in vitro systems allows us to look at tumor biology from a different angle and to build robust experimental in vivo strategies.”
The collaboration is designed as a complementary approach that connects established preclinical in vivo expertise with emerging human-based in vitro technologies. It supports more targeted, biology-driven research strategies and the principles of the 3Rs (Replace, Reduce, Refine), contributing to the ongoing shift toward more human-relevant experimental systems.
Beyond joint research, the partnership includes model development activities, elaboration of commercialization strategies, and close scientific exchange, including collaboration between early-career researchers from both organizations.
Dynamic42 and EPO will jointly present the first results of their collaboration at the American Association for Cancer Research® Annual Meeting 2026. Both companies plan to expand the collaboration further, exploring additional indications and extending the use of organ-on-chip technologies across different areas of drug development.
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.
We evolved for a linear world. If you walk for an hour, you cover a certain distance. Walk for two hours and you cover double that distance. This intuition served us well on the savannah. But it catastrophically fails when confronting AI and the core exponential trends at its heart.
From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops (floating-point operations‚ the core unit of computation) for early systems to over 10²⁶ flops for today’s largest models. This is an explosion. Everything else in AI follows from this fact.
The skeptics keep predicting walls. And they keep being wrong in the face of this epic generational compute ramp. Often, they point out that Moore’s Law is slowing. They also mention a lack of data, or they cite limitations on energy.
But when you look at the combined forces driving this revolution, the exponential trend seems quite predictable. To understand why, it’s worth looking at the complex and fast-moving reality beneath the headlines.
Think of AI training as a room full of people working calculators. For years, adding computational power meant adding more people with calculators to that room. Much of the time those workers sat idle, drumming their fingers on desks, waiting for the numbers to come through for their next calculation. Every pause was wasted potential. Today’s revolution goes beyond more and better calculators (although it delivers those); it is actually about ensuring that all those calculators never stop, and that they work together as one.
Three advances are now converging to enable this. First, the basic calculators got faster. Nvidia’s chips have delivered an over sevenfold increase in raw performance in just six years, from312 teraflops in 2020 to2,250 teraflops today. Our ownMaia 200 chip, launched this January, delivers 30% better performance per dollar than any other hardware in our fleet. Second, the numbers arrive faster thanks to a technology called HBM, or high bandwidth memory, which stacks chips vertically like tiny skyscrapers; the latest generation, HBM3, triples the bandwidth of its predecessor, feeding data to processors fast enough to keep them busy all the time. Third, the room of people with calculators became an office and then a whole campus or city. Technologies likeNVLink andInfiniBand connect hundreds of thousands of GPUs into warehouse-size supercomputers that function as single cognitive entities. A few years ago this was impossible.
These gains all come together to deliver dramatically more compute. Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore’s Law would predict only about a 5x improvement over this period. We saw 50x. We’ve gone from two GPUs training AlexNet, the image recognition model that kicked off the modern boom in deep learning in 2012, to over 100,000 GPUs in today’s largest clusters, each one individually far more powerful than its predecessors.
Then there’s the revolution in software. Research fromEpoch AI suggests that the compute required to reach a fixed performance level halves approximately every eight months, much faster than the traditional 18-to-24-month doubling of Moore’s Law. The costs of serving some recent models have collapsed by a factor of up to 900 on an annualized basis. AI is becoming radically cheaper to deploy.
The numbers for the near future are just as staggering. Consider that leading labs are growing capacity at nearly 4x annually. Since 2020, the compute used to train frontier models has grown5x every year. Global AI-relevant compute is forecast to hit 100 million H100-equivalents by 2027, a tenfold increase in three years. Put all this together and we’re looking at something like another 1,000x in effective compute by the end of 2028. It’s plausible that by 2030 we’ll bring an additional200 gigawatts of compute online every year—akin to the peak energy use of the UK, France, Germany, and Italy put together.
What does all this get us? I believe it will drive the transition from chatbots to nearly human-level agents—semiautonomous systems capable of writing code for days, carrying out weeks- and months-long projects, making calls, negotiating contracts, managing logistics. Forget basic assistants that answer questions. Think teams of AI workers that deliberate, collaborate, and execute. Right now we’re only in the foothills of this transition, and the implications stretch far beyond tech. Every industry built on cognitive work will be transformed.
The obvious constraint here is energy. A single refrigerator-size AI rack consumes 120 kilowatts, equivalent to 100 homes. But this hunger collides with another exponential: Solar costs have fallen by a factor of nearly 100 over 50 years;battery prices have dropped 97% over three decades. There is a pathway to clean scaling coming into view.
The capital is deployed. The engineering is delivering. The $100 billion clusters, the 10-gigawatt power draws, the warehouse-scale supercomputers … these are no longer science fiction. Ground is being broken for these projects now across the US and the world. As a result, we are heading toward true cognitive abundance. At Microsoft AI, this is the world our superintelligence lab is planning for and building.
Skeptics accustomed to a linear world will continue predicting diminishing returns. They will continue being surprised. The compute explosion is the technological story of our time, full stop. And it is still only just beginning.
As cancer care becomes data-driven, artificial intelligence (AI) will play an increasingly central role across the treatment continuum, from biomarker identification and drug development to clinical trial recruitment and diagnostics. In this corner of healthcare, the ability of AI to interpret and annotate tumor sample slides that have been digitized is taking center stage. While the promise is great, and AI interpretation is already influencing some clinical care, it has not yet reached critical mass.
“There’s something like a billion slides created every year for diagnostic purposes, and today most of those, about 85%, are still read by a pathologist with a microscope on physical glass slides,” said David West, CEO and co-founder of digital pathology company Proscia. In practice, that means pathologists manually examine slides, identify cancer, grade tumors, and dictate reports in a traditional approach to diagnosing cancer that has seen little change in decades.
Mohamed Omar, MD Associate Professor Cedars-Sinai Medical Center
But that foundation is now shifting. Advances in slide scanning, cloud storage, and AI are turning digital pathology images into data that can be analyzed at scale. At Memorial Sloan Kettering Cancer Center, large archives of digitized slides helped launch Paige AI, one of the earliest companies to train deep learning systems on pathology images linked to clinical and genomic outcomes. This yielded the first U.S. Food and Drug Administration (FDA)-approved diagnostic using AI and digital pathology: Paige Prostate Detect. The company, which was acquired last year by AI-enabled precision medicine company Tempus, now combines Paige’s digital pathology-based AI with Tempus’s broad genomic sequencing data platform.
Researchers in the field say the implications of AI in digital pathology extend beyond image analysis. Mohamed Omar, MD, an associate professor of computational biology at Cedars-Sinai Medical Center, Los Angeles, noted that large language models can help clinicians navigate a research landscape that produces “hundreds of papers every single day” to inform ongoing cancer research. Multimodal AI tools promise to unlock even more insights from digital pathology data by combining it with genomic, radiomic, and clinical data to build powerful new models of both common and rare cancers for diagnosis, drug development, and clinical trial enrollment.
Razik Yousfi SVP and GM, Tempus
While adoption is in its early stages, the advent of faster and less expensive scanners is bringing digital pathology within reach of both regional and rural hospitals. Razik Yousfi, senior vice president and general manager of AI products at Tempus, and a co-founder of Paige, predicts that within the next 10 years, the majority of pathology workflows will be digital. The ultimate goal of the application of AI here is not to replace human pathologists, but to empower them with a capable assistant while spreading adoption beyond major medical centers.
Building the foundations
As the field of applying AI to digital pathology progresses, it needs to build the groundwork for a wider range of potential applications that could address rare cancers and other areas without an abundance of data. One such project is called Atlas, a collaboration between researchers in Korea, Germany, and the United States to build a foundation model trained using 1.2 million histopathology whole-slide images from 490,000 cases sourced from the Mayo Clinic and Charité – Universitätsmedizin Berlin.
Foundation models like Atlas allow large-scale pre-training of data to develop numerical representations called embeddings that capture both the structural and contextual features of slides in the dataset. Atlas incorporates a diversity of diseases, staining types, and scanners, and uses multiple image magnifications during training. This broad approach confers power and utility. It allows the digitized representations of the histology to be adapted, queried, or fine-tuned to very specific downstream tasks using much less data than would be needed to build a one-off model.
As such, a foundation model provides a reusable digitized computational backbone that can be tapped across a wide range of uses, like tumor classification, detection of morphologic structures, biomarker quantification, and outcome prediction. In short, foundational models make the process of querying digital pathology images more efficient compared with past approaches.
Andrew P. Norgan, MD, PhD CMO, Mayo Clinic
“In the case of pathology, the successful AI models developed using ‘conventional’ neural network approaches before the advent of FMs (foundation models) typically required huge amounts of training data to achieve high performance and generalizability—the ability to work across datasets distinct from the training data,” said lead Atlas researcher Andrew P. Norgan, MD, PhD, CMO of Mayo Clinic Digital Pathology and assistant professor of laboratory medicine and pathology. “We think of FMs as [an] enabler that allows model development in pathology … to move from artisanal or craft processes to more scalable and reproducible processes that should allow for the rapid development of high-quality models to address problems in pathology.”
At Paige AI, the company’s early work resulted in the first FDA-approved AI diagnostic, Paige Prostate Detect. Its algorithm was built using a technique called multiple instance learning instead of traditional supervised neural network techniques that require detailed human annotation of slides, a time-consuming and expensive method that could expose the learning to human error. The difference between the two methods is that traditional neural networks expose AI to a slide with cancer and tell it that there is cancer present. In multiple instance learning, the model is shown unannotated slides and is tasked with finding the cancer.
Even this approach, however, required a very large dataset. It became apparent to company leaders that the heavy lifting required to get Paige Prostate Detect to work wasn’t scalable.
“We had kind of cracked this recipe,” said Yousfi. “We know how to use a lot of GPU (graphics processing unit) compute, and if we get a ton of data and a lot of compute, we can build anything. But GPU infrastructure is very expensive, and it takes a lot of time to train a very large system.”
Perhaps the most important factor moving Paige away from this model is that it will not work when there is only a small amount of data available. This blocks the ability to train AI to recognize rare cancers for which sample counts are low. The company needed a different approach.
“We had this idea [for] a new system that was basically trained on all of the images we had access to, independent of the organ and indication and tissue and task,” Yousfi said. “Back then, we didn’t know what that thing was called. But ultimately, that became what everyone is calling today a foundation model.”
Originally trained on 200,000 slides, Paige’s new model now includes 3.5 million images and roughly two billion parameters, making it the backbone for other downstream applications the company builds today. This ability to use foundation models as the AI and data encyclopedia for smaller applications will ultimately propel the field of digital pathology forward by widening the playing field.
Going multimodal
To address more complex predictive problems, additional data types can be integrated. Clinical, radiologic, or genomic data can be combined with morphologic embeddings or used during training to help the model learn which tissue features carry a signal of disease or identify a biomarker. These approaches aim to support precision oncology by making morphologic data computable and aligning slide-derived features with other cancer-focused datasets. “These approaches can surface subtle or ‘latent’ patterns in pathology slides and align them with other data sources,” Norgan said. Pathologist and oncology care teams can then evaluate and interpret the features identified by the models within the clinical and biological context.
“In this way, pathologists and oncology teams use these outputs as decision-support tools, while clinical judgment remains central to diagnostic interpretation and therapeutic decision making,” Norgan added.
Atlas has now been succeeded by Atlas2, which was trained on 5.5 million pathology images and is now a two billion-parameter model, making it one of the largest pathology foundation models to date. The team has explored distilling methods to create smaller, more efficient, and targeted versions of the model that retain performance, with an eye toward finding a balance between scale and deployability.
Proscia is embarking on a different multimodal approach that combines vision models with language models, with the intent of creating methods to query the morphology of digitized slides. Their efforts in vision-language models (VLMs) combine textual data with visual data and allow the model to describe the morphology of a slide, answer questions about what it contains, find images in a database based on a text query, and even follow multimodal instructions such as “circle the tumor area on this image.”
In short, a VLM can be engaged in the same way you can engage a human. “I could go ask a pathologist to point out all the areas of tumor-infiltrating lymphocytes,” West said. “Now, because language-vision models are encoding language and images in the same space, they can do that, too. You can ask the model to describe what is happening in an image, and it will tell you exactly what it sees.”
At Cedars-Sinai, Omar’s work with large language models takes a less direct route of leveraging queries to gather information from research studies or even images. “Basically, you could go to the tool, ask questions, and the tool will provide you with pieces of code,” he explained. “These pieces of code are what you use on the slide to get more information.”
Atlas provides a similar function at the Mayo Clinic, Norgan noted. Because the model-generated embeddings in the digitized slide also encode semantic information, the Atlas team is now building a slide search function, which would allow researchers or clinicians to identify and access slides, or regions of slides, with related features.
Democratizing care
Although it will take time to disseminate the tools needed for AI-enabled digitized models of cancer care to smaller health systems, the future is now at Moffitt Cancer Center, where the research hospital is engaged in a top-to-bottom digitization of its system.
Marilyn Bui, MD, PhD Senior Member Moffitt Cancer Center
According to Marilyn Bui, MD, PhD, senior member of the departments of pathology and machine learning, the comprehensive cancer center plans for full digital adoption across clinical and research labs by 2027. Last August, it entered a multi-year collaboration with integrated AI and digital pathology company PathAI to deploy its cloud-based digital pathology image management system for both research and clinical applications.
Within the pathology department, the transition will mean that all glass slides will be scanned and reviewed digitally, providing the basis for applying AI computational tools to assist pathologists. Bui said that the cancer center is accelerating its move toward clinical AI adoption: “Just today I received an email asking which AI algorithms we plan to incorporate for clinical utility—prostate cancer, breast cancer, general tumor detection,” she said. “For us, it’s no longer just research.”
Moffitt is taking a hybrid approach to algorithm development and deployment within the system. Some AI tools will come from commercial vendors and will be validated internally, while others will be developed by investigators through the center’s translational pathology work. Taking this approach will allow it to apply AI to both common cancers and the rare tumor types Moffitt frequently encounters.
While the digital initiative will be transformational, Bui emphasized that the goal is not to replace pathologists but to enhance their capabilities. She prefers to refer to AI as augmented intelligence to reflect this. “Artificial intelligence suggests a robot replacing us,” she said. “But what we mean is augmented intelligence—tools that assist and enhance our ability to make clinical decisions.”
Further, Moffitt intends to integrate digitized slide data with genomic, proteomic, and clinical outcome data to build a multimodal data environment that could advance precision oncology. “Digital pathology and AI will allow us to extract far more information from tissue samples,” Bui said, “making our diagnoses more actionable for the clinical team and ultimately improving patient care.”
The promise of AI in oncology isn’t just better algorithms, it’s broader access. The maturation of computational pathology and its dissemination from large cancer centers like Moffitt to regional and rural health systems has the potential to provide levels of care typically only available at large research hospitals in community settings as well.
“It’s about democratizing access to care,” said Omar. “For a person in Maine or Wisconsin or another place to have access to the same high-quality care that you would get from a larger academic medical center in LA or New York, slides have to be digitized.”
Over the next 10 years, there could be a compelling business case for hospitals to embrace digital pathology. As the cost of scanners comes down and a broad range of diagnostic tools becomes available, digitizing routine H&E slides could become common.
While genetic cancer testing can cost hundreds of dollars, Omar pointed out that pathology slides “cost $5 [and] they are available universally, in all patients with cancer.” As AI models increasingly identify genomic-level insights directly from those inexpensive images, it represents a “huge win for accessibility, making AI work for patients who cannot afford genetic tests,” Omar said. If there is broad adoption of digital pathology “it is very easy to roll out any kind of AI models and computational tools across the board, across situations and locations that don’t have access to care.”
“At the end of the day, all slides will be digitized,” he concluded. “It’s just a matter of time.”
Chris Anderson, a Maine native, has been a B2B editor for more than 25 years. He was the founding editor of Security Systems News and Drug Discovery News, and led the print launch and expanded coverage as editor in chief of Clinical OMICs, now named Inside Precision Medicine.
Gilead Sciences has agreed to acquire German-based Tubulis for up to $5 billion, the companies said today, in a deal designed to expand the buyer’s antibody–drug conjugate (ADC) capabilities with a focus on fighting cancer.
Headquartered in Munich, privately held Tubulis has developed next-generation ADC candidates based on its own conjugation, linker and payload technologies intended to more selectively deliver diverse payloads to tumors deemed to be of high unmet need. The companies said Tubulis’ programs and platforms have broad potential across multiple tumor types, complementing Gilead’s development and commercialization expertise in oncology.
“We like the strategic fit and deal terms of the Tubulis (private) acquisition,” Daina M. Graybosch, PhD, senior managing director, immuno-oncology and a senior research analyst at Leerink Partners, wrote this morning in a research note. “This is more than an oncology bolt-on; we see real platform value in application of Tubulis’ ADC technologies to other therapeutic areas, namely virology.”
Tubulis’ lead pipeline candidate, TUB-040, is a sodium-dependent phosphate transport protein 2B (NaPi2b)-targeting topoisomerase-I inhibitor (TOPO1i) ADC that is now under study in the Phase Ib/II NAPISTAR1-01 trial (NCT06303505) assessing its safety, pharmacokinetics, and preliminary efficacy as a treatment for platinum-resistant ovarian cancer and non-small cell lung cancer (NSCLC).
In October at the European Society for Medical Oncology (ESMO), Graybosch noted, Tubulis presented data for TUB-040 showing a confirmed 50% overall response rate (ORR) and a 60% unconfirmed ORR across dose levels and irrespective of target antigen—results that were competitive with more mature datasets from leading TOPO1i ADCs.
“Though the dataset was early, and our primary outgoing question was how durability would mature, we suspect that Gilead saw durability maturing positively in their diligence,” Graybosch added. “If TUB-040 proves active in NSCLC, the program could complement their Trodelvy and IO [immune-oncology] lung programs. We wonder if Gilead saw early clinical NSCLC data in their diligence and if excitement around the emerging signal drove some of Tubulis’ valuation.”
Another Tubulis pipeline candidate, TUB-030, is a 5T4-targeting ADC that according to the companies has shown promising initial clinical data across various solid tumor types. TUB-030 is currently under study in the Phase I/IIa 5-STAR 1-01 trial (NCT06657222), a first-in-human study which aims to evaluate the safety, tolerability, pharmacokinetics, and efficacy of TUB-030 as a monotherapy in patients with advanced solid tumors. Tubulis has said it is developing TUB-030 for up to 13 undisclosed solid tumor indications.
Partners since 2024
The acquisition deal follows a two-year, up-to-$465 million collaboration with Tubulis launched in December 2024. Gilead gained access to Tubulis’ Tubutecan and Alco5 platforms after signing an exclusive option and license agreement to discover and develop an ADC against a solid tumor target.
At the time, Gilead agreed to pay Tubulis $20 million upfront, received an option that if exercised would have given Tubulis an additional $30 million—plus up to $415 million in payments tied to achieving development and commercialization milestones, as well as mid-single to low double-digit tiered royalties on sales of marketed products resulting from the collaboration.
“Today’s agreement follows a two-year collaboration with Tubulis, which has given us strong conviction in their programs and research capabilities,” Gilead Chairman and CEO Daniel O’Day said in a statement. “The agreement to acquire Tubulis is a significant milestone in Gilead’s progress in oncology. The company brings a clinical-stage candidate that is a potential new treatment for ovarian cancer, as well as a next-generation ADC platform and a promising early pipeline.”
“Bringing this potential into Gilead would further expand what is already the strongest and most diverse pipeline in our company’s history,” O’Day declared.
Investors appeared less enthusiastic about the acquisition, as shares of Gilead dipped 1.7% in early Tuesday trading to $137.80 as of 12:01 p.m. ET.
Tubulis is Gilead’s third announced acquisition this year. The biotech giant announced plans in March to buy Ouro Medicines for up to $2.18 billion, and in February agreed to acquire Arcellx for up to $7.8 billion—for which it agreed last week to extend its tender offer until 5 p.m. ET on April 24.
Under the acquisition deal, Gilead agreed to acquire all of the outstanding equity of Tubulis for $3.15 billion in upfront cash payable at closing, and up to $1.85 billion in payments tied to milestones.
The transaction is expected to close in the second quarter subject to expiration or termination of specified regulatory filings and other customary conditions.
Upon closing of the deal, Tubulis will operate as a dedicated ADC research organization within Gilead, with the Munich site serving as a hub for ADC innovation, building on its integrated discovery, manufacturing, and clinical capabilities to advance next generation ADCs.
Gilead said it plans to finance the transaction with a combination of cash on hand and senior unsecured notes. Gilead finished 2025 with $10.605 billion of cash, cash equivalents and marketable debt securities, up from $9.991 billion as of December 31, 2024.
Since the first in vitro fertilization (IVF) baby was born in 1978, the options for couples or individuals struggling with infertility have improved exponentially. However, the core methods that make up this process are still fairly crude and associated with significant discomfort and side effects. This is something that reproductive endocrinologist Marie-Christine Maurel, PhD, chief scientific officer (CSO) and founder of Igyxos Biotherapeutics, is hoping to improve with the company’s first‑in‑class antibody treatment for infertility.
The antibody treatment—IGX12—amplifies the body’s own follicle-stimulating hormone (FSH) signal in both women and men, potentially improving production of both sperm and eggs with fewer injections than IVF and more physiological control.
Maurel has a doctorate in reproductive physiology from Pierre & Marie Curie University in Paris and worked as a post-doctoral fellow at the Pasteur Institute in Paris. The idea for the infertility treatment, which achieved promising Phase I results at the end of last year, originated from work at Maurel’s first biotech firm ReproPharm, which she co-founded in 2009 after winning a French “national competition for the creation of innovative companies.”
Maurel previously worked at the National Institute of Agronomic Research (INRA) near Paris for more than 25 years. Her group studied how gonadotropins affect fertility in animals and developed monoclonal antibodies that impact the activity of these reproductive hormones. ReproPharm initially used this research to improve fertility in farm animals, but when the team realized the same ideas could be applied to human infertility, the initial company was split into ReproPharm Vet and Igyxos in 2017 to focus on animal and human fertility problems, respectively. Alongside serving as CSO at Igyxos, Maurel remains president and CEO of ReproPharm Vet.
Maurel discussed her inspirations, research, and motivations for founding both companies with Inside Precision Medicine’s senior editor, Helen Albert, and outlined why Igyxos’s antibody could be so important if it achieves market authorization.
Q: What inspired you to become a scientist?
Marie-Christine Maurel, PhD
Maurel: When I was younger, I was passionate about science, and biology in particular. In my teens, I hesitated between choosing to study medicine or biological research. Finally, I chose biological research, but remained very interested in biomedicine. Currently, I mix both topics because we are developing a new medicine to treat infertility problems in humans, so it’s a mix of research and medicine. I still enjoy scientific research, so I don’t regret my decision.
Q: You worked in academia for a long time before you decided to make the move into industry. What inspired you to do that?
Maurel: In nature, there are lots of things that occur. You just have to discover them and know how to ask the right questions to understand how they work. Penicillin is an extraordinary example. And here it’s the same thing. We were doing experiments with sheep and goats. When we discovered that the ewes or does that secreted potentiating antibodies were hyper prolific and had high numbers of offspring, we wondered why. Normally, antibodies block the activity of a hormone and never enhance it. But in this case, we discovered that these particular antibodies were able to potentiate the activity of reproductive hormones. It was a marvelous result because it meant [that] it was possible to avoid the use of hormone treatment in animals. When my team and I discovered the existence of these potentiating antibodies, I quickly assessed their potential for application in both human and animal health. I wanted to develop and translate the research. The fact that there are potentiating antibodies for FSH is extraordinary, because antibodies are normally always inhibitory. I wanted to develop a potentiating antibody so we could have much more effective treatments for infertility. I also won a national competition for the creation of innovative companies, which helped with founding ReproPharm. It was a wonderful adventure to create a new biotech with our innovation.
Q: What did you learn from the experience of founding ReproPharm?
Maurel: I learned a lot of things. It was a human experience. I met a lot of people in medicine and industry who were very important for the development of the company and myself as well. These people helped me to build and to progress the company. Building a good network was important for me when I went into industry. I would also advise this for young people who want to create a biotech company. Meeting good people helps enormously!
Q: What made you decide to split ReproPharm into the two spinout companies, ReproPharm Vet and Igyxos Biotherapeutics?
Maurel: My research group was based at INRA initially. It’s a French academic research center focused on animal reproduction. We started with an animal reproduction problem linked to breeding ovine and caprine species, but early on, we tried our innovation on human hormones because we thought it could be an excellent approach to treat infertility problems in women. We developed an antibody against human FSH to see if we could enhance the activity of human FSH and in animal species. We got some money to carry out the first experiments and had very good results. We then decided to develop this innovation in human health, but needed more funds to develop it further. All our existing investors told us that they were unwilling to take the risk of investing in a company developing both veterinary and human medicine. It was impossible for them because it was not separated, so we decided to split the first company into two independent companies in 2017.
Q: Did any of your experiences at ReproPharm help you to do things better at Igyxos?
Maurel: First, I can say that at Igyxos, from the experience with ReproPharm, I wanted to do as much research and development on IGX12 as possible using our own funds, and license the therapy as late as possible because that gives us more freedom to develop it as we want to. I think it is necessary to be independent as long as possible for this reason.
Also, during the founding and development of ReproPharm, we developed a lot of animal models, which are very useful now to develop IGX12 for treating human infertility, both in men and women. So it was a very strong basis for Igyxos. All these animal models we developed at ReproPharm were important for developing IGX12 and getting it to clinical trials.
Q: Can you tell me a bit more about what you’re trying to achieve at Igyxos?
Maurel: FSH is exactly the same hormone in men and women. It has different target cells, but the molecule is the same. So one potentiating antibody could act on FSH either in men or women. It’s exactly the same mechanism of action, so we can develop the first treatment in men with oligozoospermia, for example.
We also want to develop a new and innovative treatment for women with infertility, which could be more efficient than current treatments that are burdensome and costly. Now it’s necessary to repeat the same hormone treatment four or five times to have a baby with a 50% chance of success. We think that it will not be necessary to repeat our treatment because we have a lot of proof of concept in animals. We have shown we can get better gametogenesis with better quality of ovulation than other methods.
Q: You reported Phase I results in December 2025. Were you happy with the findings?
Maurel: Yes, it was totally successful. We got very nice results. No adverse events, and we have some first efficacy results, so we can start Phase II trials, but we need to raise money first.
The trial results have helped to interest investors, and we are now in contact with several funds. If the fundraising is successful, we hope to be able to start Phase II trials soon.
Q: You mentioned that IGX12, if approved, would be the first such treatment for men with common fertility issues like oligozoospermia. Why have more treatments not been developed for men before?
Maurel: The problem of male infertility was not considered for a long, long time, perhaps because of cultural issues. Now there is a huge problem with infertility in men because sperm counts are decreasing. Numbers decreased from around 100 million per mL to 50 million per mL between 1973 and 2018. So this treatment is very necessary!
Q: Do you think that if your treatment is successful, it could make IVF more accessible?
Maurel: Yes, I think that it would allow a reduction in both time and economic cost, because as I said previously, the treatment will be more efficient, so no need to repeat it. We developed the concept that the antibody could act on the endogenous FSH. So, using our approach, it would not be necessary for women to inject FSH, because the antibody is able to boost the woman’s own FSH. In the animal health domain, we use the antibody only. We never inject endogenous hormones, so it’s very clean. In humans we will also only inject the antibody. We never inject FSH. So it’s a single injection per month. If we succeed, it’s a very big market and a very nice treatment for a lot of people.
Q: Could IGX12 make fertility treatment more targeted for specific people or certain population groups?
Maurel: Yes, for example, men with oligozoospermia. That means the sperm count is too low for natural conception. If it’s a very low level, it’s not even possible to do IVF. So we will target this category of men. In women, we will target those who don’t have a good predicted result with IVF, for example, if they have a low follicular count. So we plan to target these two populations, which have few chances to succeed at having children with current treatments.
Q: What are your future plans for Igyxos and ReproPharm Vet?
Maurel: For Igyxos, the current priority is to raise funds to start Phase II clinical trials, both in men and women. For ReproPharm Vet, the objective is to conclude an ongoing collaboration with a big veterinary and pharma partner.
Helen Albert is senior editor at Inside Precision Medicine and a freelance science journalist. Prior to going freelance, she was editor-in-chief at Labiotech, an English-language, digital publication based in Berlin focusing on the European biotech industry. Before moving to Germany, she worked at a range of different science and health-focused publications in London. She was editor of The Biochemist magazine and blog, but also worked as a senior reporter at Springer Nature’s medwireNews for a number of years, as well as freelancing for various international publications. She has written for New Scientist, Chemistry World, Biodesigned, The BMJ, Forbes, Science Business, Cosmos magazine, and GEN. Helen has academic degrees in genetics and anthropology, and also spent some time early in her career working at the Sanger Institute in Cambridge before deciding to move into journalism.
Background: Men face a substantially higher risk of suicide. Effective suicide prevention strategies for men should specifically target gender-related risk factors, such as their lower likelihood of seeking professional help. Objective: This study investigates the use and impact of a suicide prevention website for men between March 1, 2023, and December 31, 2024. The Männer Stärken website is the first suicide prevention platform for men in Germany, with the primary aim of facilitating help-seeking behavior. The development of the platform was informed by interviews with men who had attempted suicide, as well as by existing evidence on effective communication strategies for engaging men at risk. Methods: This exploratory study combines quantitative web analytics and survey data with a qualitative analysis of open-ended responses from a feedback form. Using the web analytics tool Matomo, data were collected on the number of visits to the website and the subpages they accessed. In addition, 291 anonymous feedback forms were analyzed regarding visitors’ perceptions of the website’s helpfulness and its potential to support help-seeking behavior. A further component involved an online survey (n=40) examining whether a short suicide prevention film featured on the website could increase the intention to seek help. Results: During the study period, the website recorded 29,279 visits. A majority (n=291) of the respondents reported via the feedback form that they found the website helpful (n=201, 69.1%) and believed it could encourage help-seeking behavior (59.8%). In the evaluation of the short film, a significant increase in participants’ intentions to seek help was observed in situations involving suicidal ideation and personal difficulties and when considering professional support services. This effect was not observed with regard to informal sources of support, such as friends or family. Conclusions: The data suggest that the website is being used. Among those who completed the anonymous survey form (N=291), a majority reported that the website fulfills its primary aim of providing helpful pathways to support services. The evaluation of the short film further supports this conclusion. However, certain limitations must be acknowledged: since the data were collected in a field setting, the ability to draw firm conclusions about the characteristics or representativeness of the visitor sample is limited. In addition, the sample size for the short film evaluation was small. Nevertheless, the findings point to a clear need for gender-specific suicide prevention initiatives. They indicate promising directions for the development of effective, low-threshold measures, which merit further investigation in future research.
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