The problem with thinking you’re part Neanderthal

You’ve probably heard some version of this idea before: that many of us have an “inner Neanderthal.” That is to say, around 45,000 years ago, when Homo sapiens first arrived in Europe, they met members of a cousin species—the broad-browed, heavier-set Neanderthals—and, well, one thing led to another, which is why some people now carry a small amount of Neanderthal DNA. 

This DNA is arguably the 21st century’s most celebrated discovery in human evolution. It has been connected to all kinds of traits and health conditions, and it helped win the Swedish geneticist Svante Pääbo a Nobel Prize.

But in 2024, a pair of French population geneticists called into question the foundation of the popular and pervasive theory. 

Lounès Chikhi and Rémi Tournebize, then colleagues at the Université de Toulouse, proposed an alternative explanation for the very same genomic patterns. The problem, they said, was that the original evidence for the inner Neanderthal was based on a statistical assumption: that humans, Neanderthals, and their ancestors all mated randomly in huge, continent-size populations. That meant a person in South Africa was just as likely to reproduce with a person in West Africa or East Africa as with someone from their own community. 

Archaeological, genetic, and fossil evidence all shows, though, that Homo ­sapiens evolved in Africa in smaller groups, cut off from one another by deserts, mountains, and cultural divides. People sometimes crossed those barriers, but more often they partnered up within them. 

In the terminology of the field, this dynamic is called population structure. Because of structure, genes do not spread evenly through a population but can concentrate in some places and be totally absent from others. The human gene pool is not so much an Olympic-size swimming pool as a complex network of tidal pools whose connectivity ebbs and flows over time.

This dynamic greatly complicates the math at the heart of evolutionary biology, which long relied on assumptions like randomly mating populations to extract general principles from limited data. If you take structure into account, Chikhi told me recently, then there are other ways to explain the DNA that some living people share with Neanderthals—ways that don’t require any interspecies sex at all.

“I believe most species are spatially organized and structured in different, complex ways,” says Chikhi, who has researched population structure for more than two decades and has also studied lemurs, orangutans, and island birds. “It’s a general failure of our field that we do not compare our results in a clear way with alternative scenarios.” (Pääbo did not respond to multiple requests for comment.)

The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells.

Chikhi and Tournebize’s argument is about population structure, yes, but at heart, it is actually one about methods—how modern evolutionary science deploys computer models and statistical techniques to make sense of mountains upon mountains of genetic data. 

They’re not the only scientists who are worried. “People think we really understand how genomes evolve and can write sophisticated algorithms for saying what happened,” says William Amos, a University of Cambridge population geneticist who has been critical of the “inner Neanderthal” theory. But, he adds, those models are “based on simple assumptions that are often wrong.” 

And if they’re wrong, what’s at stake is far more than a single evolutionary mystery. 

A captivating story of interspecies passion

Back in 2010, Pääbo’s lab pulled off something of a miracle. The researchers were able to extract DNA from nuclei in the cells of 40,000-year-old Neanderthal bones. DNA breaks down quickly after death, but the group got enough of it from three different individuals to produce a draft sequence of the entire Neanderthal genome, with 4 billion base pairs. 

As part of their study, they performed a statistical test comparing their Neanderthal genome with the genomes of five present-day people from different parts of the world. That’s how they discovered that modern humans of non-African ancestry had a small amount of DNA in common with Neanderthals, a species that diverged from the Homo sapiens line more than 400,000 years ago, that they did not share with either modern humans of African ancestry or our closest living relative, the chimpanzee. 

Neanderthal front and profile view
This model of a Neanderthal man was exhibited in the “Prehistory Gallery” at London’s Wellcome Historical Medical Museum in the 1930s.
WELLCOME COLLECTION

Pääbo’s team interpreted this as evidence of sexual reproduction between ancient Homo sapiens and the Neanderthals they encountered after they expanded out of Africa. “Neanderthals are not totally extinct,” Pääbo said to the BBC in 2010. “In some of us, they live on a little bit.”

The discovery was monumental on its own—but even more so because it reversed a previous consensus. More than a decade earlier, in 1997, Pääbo had sequenced a much smaller amount of Neanderthal DNA, in that case from a cell structure called a mitochondrion. It was different enough from Homo sapiens mitochondrial DNA for his team to cautiously conclude there had been “little or no interbreeding” between the two species. 

After 2010, though, the idea of hybridization, also called admixture, effectively became canon. Top journals like Science and Nature published study after study on the inner Neanderthal. Some scientists have argued that Homo sapiens would never have adapted to colder habitats in Europe and Asia without an infusion of Neanderthal DNA. Other research teams used Pääbo’s techniques to find genetic traces of interbreeding with an extinct group of hominins in Asia, called the Denisovans, and a mysterious “ghost lineage” in Africa. Biologists used similar tests to find evidence of interbreeding between chimpanzees and bonobos, polar and brown bears, and all kinds of other animals. 

The inner-Neanderthal hypothesis also took a turn for the personal. Various studies linked Neanderthal DNA to a head-spinning range of conditions: alcoholism, asthma, autism, ADHD, depression, diabetes, heart disease, skin cancer, and severe covid-19. Some researchers suggested that Neanderthal DNA had an impact on hair and skin color, while others assigned individuals a “NeanderScore” that was correlated with skull shape and prevalence of schizophrenia markers. Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports. 

The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells. Or as Latif Nasser, a host of the popular-science program Radiolab, put it when he was hospitalized with Crohn’s disease, another Neanderthal-associated condition: “I just keep imagining these tiny Neanderthals … just, like, stabbing me and drawing these little droplets of blood out of me.”

“These things become meaningful to people,” Chikhi says. “What we say will be important to how people view themselves.” 

The pitfalls of simplistic solutions 

When population geneticists built the theoretical framework for evolutionary biology in the early 20th century, genes were only abstract units of heredity inferred from experiments with peas and fruit flies. Population genetics developed theory far more quickly than it accumulated data. As a result, many data-driven scientists dismissed the study of evolution as a form of storytelling based on unexamined assumptions and preconceived ideas.

By the ’90s, though, genes were no longer abstractions but sequenced segments of DNA. Genomic sequencing grounded evolutionary studies in the kind of hard data that a chemist or physicist could respect. 

Yet biologists could not simply read evolutionary history from genomes as though they were books. They were trying to determine which of a nearly infinite number of plausible histories was the most likely to have created the patterns they observed in a small sample of genomes. For that, they needed simplified, algorithmic models of evolution. The study of evolution shifted from storytelling to statistics, and from biology to computer science. 

That suited Chikhi, who as a child was drawn to the predictable laws and numerical precision of math and science. He entered the field in the mid-’90s just as the first big studies of human DNA were settling old debates about human origins. DNA showed that Africa harbored far more genetic diversity than the entire rest of the planet. The new evidence supported the idea that modern humans evolved for hundreds of thousands of years in Africa and expanded to the other continents only in the last 100,000 years. For Chikhi, whose parents were Algerian immigrants, this discovery was a powerful challenge to the way some archaeologists and biologists talked about race. DNA could be used to deconstruct rather than encourage the pernicious idea that human races had deep-seated evolutionary differences based on their places of origin. 

At the same time, though, he was wary of the tendency to treat DNA as the final verdict on open questions in evolution. Chikhi had been surprised when, back in 1997, Pääbo and his team used that small amount of mitochondrial DNA to rule out hybridization between Homo sapiens and Neanderthals. He didn’t think that the absence of Neanderthal DNA there necessarily meant it wouldn’t be found elsewhere in the Homo sapiens genome.

Chikhi’s own research in the aughts opened his eyes to the gaps between historical reality and models of evolution. For one, despite the assumption of random mating, none of the animals Chikhi studied actually mated randomly. Orangutans lived in highly fragmented habitats, which restricted their pool of potential mates, and female birds were often extremely picky about their male partners. 

These factors could confound an evolutionary biologist’s traditional statistical tool kit. Scientists were starting to apply a mathematical technique to estimate historical population sizes for a species from the genome of just a single individual. This method showed sharp population declines in the histories of many different species. Chikhi realized, though, that the apparent declines could be an artifact of treating a structured population as one that evolved with random mating; in that case, the technique could indicate a bottleneck even if all the subgroups were actually growing in size. “This is completely counterintuitive,” he says. 

That’s at least partly why, when Pääbo’s 2010 Neanderthal genome came out, Chikhi was impressed with the sheer technical accomplishment but also leery of the findings about hybridization. “It was the type of thing we conclude too quickly based on genetic data,” he says. Pääbo’s work mentioned population structure as a possible alternative explanation—but didn’t follow up.

Just a couple of years later, a pair of independent scientists named Anders Eriksson and Andrea Manica picked up the idea, building a model with simple population structure that explicitly excluded admixture. They simulated human evolution starting from 500,000 years ago and found that their model produced the same genomic patterns Pääbo’s group had interpreted as evidence of hybridization.

“Working with structured models is really out of the comfort zone of a lot of population geneticists,” says Eriksson, now a professor at the University of Tartu in Estonia.

Their research impressed Chikhi. “At the time, I thought people would focus on population structure in the evolution of humans,” he says. Instead, he watched as the inner-Neanderthal hypothesis took on a life of its own. Scientists produced new methods to quantify hybridization but rarely examined whether population structure would yield the same results. To Chikhi, this wasn’t science; it was storytelling, like some of the old narratives about the evolution of racial differences. 

Chikhi and Tournebize decided to take a crack at the problem themselves. “I’ve always been very skeptical about science, and population genetics in particular,” says Tournebize, now a researcher at the French National Research Institute for Sustainable Development. “We make a lot of assumptions, and the models we use are very simplistic.” As detailed in a 2024 paper published in Nature Ecology & Evolution, they built a model of human evolution that replaced randomly mating continent-wide populations with many smaller populations linked by occasional migration. Then they let it run—a million times.

At the end of the simulation, they kept the 20 scenarios that produced genomes most similar to the ones in a sample of actual Homo sapiens and Neanderthals. Many of these scenarios produced long segments of DNA like the ones their peers argued could only have been inherited from Neanderthals. They showed that several statistics, which other scientists had proposed as measurements of Neanderthal DNA, couldn’t actually distinguish between hybridization and population structure. What’s more, they showed that many of the models that supported hybridization failed to accurately predict other known features of human evolution.

“A model will say there was admixture but then predict diversity that is totally incompatible with what we actually know of human diversity,” Chikhi says. “Nobody seems to care.”

So how did Neanderthal DNA wind up in living people if not via interspecies passion? Chikhi and Tournebize think it’s more likely that it was inherited by both Neanderthals and some sapiens groups in Africa from a common ancestor living at least half a million years ago. If the sapiens groups carrying those genetic variants included the people who migrated out of Africa, then the two human species would have already had the DNA in common when they came into contact in Europe and Asia—no sex required. 

“The interpretation of genetic data is not straightforward,” Chikhi says. “We always have to make assumptions. Nobody takes data and magically comes up with a solution.” 

Embracing the uncertainty 

Most of the half-dozen population geneticists I spoke with praised Chikhi and Tournebize’s ingenuity and appreciated the spirit of their critique. “Their paper forces us to think more critically about the model we use for inference and consider alternatives,” says Aaron Ragsdale, a population geneticist at the University of Wisconsin–Madison. His own work likewise suggests that the earliest Homo sapiens populations in Africa were probably structured—and that this is the likely reason for genomic patterns that other research groups had attributed to hybridization with a mysterious “ghost lineage” of hominins in Africa.

Yet most researchers still believe that modern humans and Neanderthals did probably have children with each other tens of thousands of years ago. Several pointed to the fact that fossil DNA of Homo sapiens who died thousands of years ago had longer chunks of apparent Neanderthal DNA than living people, which is exactly what you would expect if they had a more recent Neanderthal ancestor. (To address this possibility, Chikhi and Tournebize included DNA from 10 ancient humans in their study and found that most of them fit the structured model.) And while the Harvard population geneticist David Reich, who helped design the statistical test from Pääbo’s 2010 study, declined an interview, he did say he thought Chikhi and Tournebize’s model was “weak” and “very contrived,” adding that “there are multiple lines of evidence for Neanderthal admixture into modern humans that make the evidence for this overwhelming.” (Two other authors of that study, Richard Green and Nick Patterson, did not respond to requests for comment.) 

Nevertheless, most scientists these days welcome the development of structured, or “spatially explicit,” models that account for the fact that any given member of a population is usually more closely related to individuals living nearby than to those living far away. 

Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history.

Other scientists also say that random mating isn’t the only assumption in population genetics that merits scrutiny. Models rarely factor in natural selection, which can also create genetic patterns that look like hybridization. Another common assumption is that everyone’s DNA mutates at the same, constant rate. “All the theory says the mutation rate is fixed,” says Amos, the Cambridge population geneticist. But he thinks that rate would have slowed drastically in the group of Homo sapiens that expanded to Europe around 45,000 years ago. This, too, could have created genomic patterns that other scientists interpret as evidence of interbreeding with Neanderthals. 

phone with dna testing results and a cartoon neanderthal that says, "Hey Eric! You have more Neanderthal DNA than 96% of other customers."
Commercial genetic testing companies like 23andMe started offering customers Neanderthal ancestry reports.
COURTESY OF 23ANDME

The point here isn’t that a complex model of evolution with many moving pieces is necessarily better than a simple one. Scientists need to reduce complexity in order to see the underlying processes more clearly. But simple models require assumptions, and scientists need to reevaluate those assumptions in light of what they learn. “As you get more data, you can justify more complex models of the world,” says Mark Thomas, a population geneticist at University College London, who wrote a history of random mating in population genetics that highlighted how the field was starting to see it as “a limiting assumption as opposed to a simplifying one.” 

It can feel discouraging to couch conversations about the past in confusing terms like “population structure” and “mutation rates.” It seems almost antithetical to the spirit of science to talk more about uncertainty at the same time we are developing powerful technologies and enormous data sets for analyzing evolution. These tools often yield novel answers, but they can also limit the questions we ask. The French archaeologist Ludovic Slimak, for example, has complained that the idea of the inner Neanderthal has domesticated our image of Neanderthals and made it difficult to imagine their humanity as distinct from our own. Investigating Neanderthal DNA is sexier to many young researchers than searching for archaeological and fossil evidence of how Neanderthals actually lived. 

Loosening our attachment to certain narratives of evolution can create space for wonder at the sheer complexity of life’s history. Ultimately, that’s what Chikhi and Tournebize hope to do. After all, they don’t believe the question of population structure versus hybridization is either-or. It’s possible, and even likely, that both played a role in human evolution. “Our structured model does not necessarily mean that no admixture ever took place,” Chikhi and Tournebize wrote in their study. “What our results suggest is that, if admixture ever occurred, it is currently hard to identify using existing methods.” 

Future methods might disentangle the different factors, but it’s just as important, Chikhi says, for scientists to be up-front about their assumptions and test alternatives. “There’s still so much uncertainty on so many aspects of the demographic history of Neanderthals and Homo sapiens,” he notes. 

Keep that in mind the next time you read about your inner Neanderthal. The association between this DNA and some diseases may be real, of course—but would journals publish these studies without the additional claim that the DNA is from Neanderthals? Any good storyteller knows that sex sells, even in science. 

Ben Crair is a science and travel writer based in Berlin.

StockWatch: IPO Market Shows Sign of Life with Avalyn Filing

The initial public offering (IPO) market showed signs of life for the first time in more than a month as Boston-based Avalyn Pharma filed a registration statement on Wednesday seeking to raise capital to develop its pipeline of respiratory disease treatments.

It’s too early to know how much money Avalyn plans to raise—the registration statement includes a placeholder “$100 million” figure that will inevitably be revised, and doesn’t say how many shares will be sold. It’s also too soon to know how much of the proceeds will go toward each of the three pipeline candidates cited in the filing to the U.S. Securities and Exchange Commission:

  • AP01—An inhaled version of pirfenidone, a small molecule modulator of cytokines and growth factors whose development the IPO would advance through Phase IIb topline data and into Phase III. AP01 is under study in the Phase IIb MIST trial (NCT06329401) as a potential treatment for progressive pulmonary fibrosis.
  • AP02—An inhaled version of nintedanib, a small molecule inhibitor of multiple tyrosine kinases, being developed to treat idiopathic pulmonary fibrosis (IPF). Avalyn plans to advance AP02 into the Phase II AURA-IPF trial (NCT07194382) after completing single-ascending dose (SAD) and multiple-ascending dose (MAD) Phase I trials in healthy adult volunteers and IPF patients.
  • AP03—A preclinical inhaled fixed-dose combination of AP01 and AP02 designed to combine what Avalyn says is their ability to substantially reduce or eliminate the adverse effects of oral pirfenidone and oral nintedanib.

Pirfenidone is an IPF drug marketed as Esbriet® by Genentech, a member of the Roche Group, with several other companies selling generic versions. Nintedanib is a kinase inhibitor with indications in treating IPF and chronic fibrosing interstitial lung diseases (ILDs) and slowing the rate of decline in pulmonary function in adults, marketed as Ofev® by Boehringer Ingelheim, with generic versions approved this month.

“The change we aim to make in the treatment paradigm of pulmonary fibrosis and other ILDs parallels the decades-long evolution seen in the treatment of asthma and COPD,” Avalyn stated in its S-1 statement.

In those diseases, the company explained, treatments advanced from broad, systemic oral therapies to targeted inhaled treatments, and ultimately to combination inhalers.

Pulmonary fibrosis “opportunity”

“We see a similar opportunity in pulmonary fibrosis, where the field still relies on oral antifibrotics today. Our programs are designed to drive a similar evolution, first by shifting treatment toward inhaled, lung-targeted formulations of existing antifibrotics that aim to improve safety and efficacy,” Avalyn explained. “We aspire to deliver inhaled therapies that combine complementary mechanisms into a single device for even greater therapeutic impact.”

In discussing the use of its proceeds, Avalyn said it envisioned advancing AP01 and AP02 through Phase IIb and Phase II topline data, respectively, into Phase III trials. AP03 would be advanced into the clinic and Phase I topline data using capital from the IPO.

Whatever isn’t spent on the pipeline candidates will be set aside for R&D activities for additional programs, working capital, and general corporate purposes, Avalyn added.

Avalyn is the first biotech IPO filing since Generate: Biomedicines completed the year’s largest to date, raising $400 million in gross proceeds toward clinical trials, as well as platform and pipeline R&D efforts. To date, seven companies have completed biotech IPOs, raising just over $1.7 billion in combined proceeds, Jefferies analyst Andrew Tsai wrote in a research note.

“The IPO market has been more of a laggard but showed signs of strength this quarter, with Q1 offerings the largest in the past four years,” Tsai wrote. As a result, he added, the IPO market is on pace to exceed historical levels except for the 2020–2021 IPO boom due to the COVID-19 pandemic.

Mixed on IPO improvement

Heading into 2026, analysts were mixed on whether this year would see improvement in the IPO market compared to 2025, when 11 U.S. companies raised a total of $3 billion on Wall Street. “We think it will be slightly better, but we have not seen enough to suggest that it’s truly rebounding,” Subin Baral, EY global life sciences deals leader, told GEN.

However, Michael Allwin, head of biopharma investment banking, Truist Securities, told GEN that IPOs are typically “the last shoe to drop” after other non-IPO financings show signs of recovery, giving him hope and optimism that 2026 would see a much more active IPO market than 2025: “While we’re not anticipating a resurgence in activity to the tune of what we saw at all-time highs in 2020 and 2021, we are anticipating a more normalized level of activity, maybe on parity with 2019.”

As for Avalyn, should its planned IPO raise the placeholder $100 million amount, it would nearly double the $138.359 million in cash, cash equivalents, and marketable securities with which Avalyn finished 2025.

Avalyn ended last year with no revenue and a net loss of $85.204 million, a 71% increase over the $49.744 million net loss the company reported for 2024. As a result, Avalyn’s accumulated deficit rose from $180.2 million at the end of 2024 to $265.4 million on December 31, 2025.

The IPO comes nine months after Avalyn completed its last financing, an oversubscribed $100 million Series D round completed in July and led by investment firms Suvretta Capital Management and SR One.

Survetta and SR One are two of 18 firms that have invested in Avalyn. The 18 include Novo Holdings, the asset manager of the foundation that controls Novo Nordisk.

Novavax rises on shareholders’ opposition

Novavax (NASDAQ: NVAX) enjoyed a small but noticeable surge in its stock price this past week after its second-largest shareholder ramped up its opposition to the vaccine developer’s leadership on several fronts.

Shah Capital Opportunity Fund, which holds an approximately 9% stake in Novavax, said it will oppose the company’s nominees for re-election to the board of directors when Novavax holds its annual shareholder meeting, scheduled for June (no date had been announced at deadline).

In an open letter to Novavax’s board, Raleigh, NC-based Shah Capital also requested that Novavax:

  • Shrink the board from eight to five members and elect new members “with emphasis on pragmatic entrepreneurial experience to turn Novavax into an equity success story.”
  • Buy back 10 to 20 million shares.
  • Retire its outstanding $225 million convertible bond with cash on hand “at the earliest.” Novavax reported $244.213 million in convertible notes payable and $240.634 million in cash and cash equivalents as of December 31, 2025.
  • Persuade a strategic long-term investor to take a 10–20% ownership stake “to reshape Novavax entirely.”

Shah Capital cited a 27% drop in Novavax’s share price from $11 on January 1, 2023, when John C. Jacobs took over as president and CEO, to $8 on March 31, 2026. The fund also expressed frustration that the COVID-19/influenza combination vaccine Novavax is developing with Sanofi (Euronext Paris: SAN)—a potential $5+ billion category, according to Shah Capital—hasn’t yet begun Phase III trials. Sanofi shared positive Phase I/II data in December and told Novavax it is working with regulators on next steps.

“Management has failed to implement aggressive cost-cutting measures necessary to achieve consistent profitability,” Himanshu H. Shah, the fund’s managing partner and chief investment officer, advocated in an open letter to Novavax’s board. “The current senior management team should be reduced by 30% to reflect Novavax’s new royalty and partnership business model.”

“The board size should also be reduced to five from eight, including electing new members with emphasis on pragmatic entrepreneurial experience to turn Novavax into an equity success story,” Shah added.

At odds for months

Shah has been at odds with Jacobs and Novavax leadership for months, having called for a sale of the company last October. Shah has held off pursuing a proxy campaign since the board’s majority has favored current management.

Novavax is based in Gaithersburg, MD, and reported approximately 749 employees as of December 31, 2025, down 21% from 952 a year earlier, according to Form 10-K annual reports.

Novavax investors responded to the Shah Capital letter with a buying spurt that sent shares climbing 5.5% Wednesday, from $7.98 to $8.42 after rising to $8.60 during intraday trading. The momentum continued somewhat on Thursday as shares rose another 1.4%, to $8.54, though Novavax slumped 5% Friday to finish the week at $8.12.

Shah Capital’s letter also sparked a statement to GEN and other news outlets from Novavax, which asserted that its board and management team “are committed to progressing our growth strategy, which is designed to leverage partnerships and R&D innovation to maximize the value of our technology.”

The statement cited recent Novavax efforts that include its up-to-$530 million (plus royalties) partnership with Pfizer (NYSE: PFE), which entered into a non-exclusive license agreement with Pfizer for use of Novavax’s Matrix-M® adjuvant; additional and expanded material transfer agreements with pharmaceuticals; and what the company called “continued progress” on its partnership with Sanofi, from which Novavax generated $225 million in milestone payments last year.

“In addition, we continue to make targeted investments in R&D with the intention of driving further value from our technology, while continuing to significantly reduce costs in our lean and efficient operating model,” Novavax continued. “We maintain constructive dialogue with our shareholders, and we welcome collaborative input that is in the best interest of Novavax and all of its shareholders.”

Shah essentially controls 14,845,097 shares of Novavax stock, including 125,359 shares he owns personally, and 14,719,738 shares owned by Shah Capital and its investment adviser.

Leaders and laggards

  • Invivyd (NASDAQ: IVVD) shares jumped 32% from $1.35 to $1.78 Thursday after the company announced positive progress in its REVOLUTION clinical program for VYD2311, a monoclonal antibody candidate designed to prevent symptomatic COVID-19. As of April 6, when the first 1,500 of 1,818 subjects reached Day 45, clinical events supported statistical powering for the high end of anticipated VYD2311 efficacy levels in the Phase III DECLARATION trial (NCT07298434), with about half of the base study still to be carried out, Invivyd said. DECLARATION will enroll ~500 additional subjects, which, according to the company, will likely, depending on recruitment rates, push back the timing for data release by approximately two months, from mid-year to Q3 2026. Invivyd also announced the discovery and advancement of a “highly potent,” half-life-extended, high-resistance-barrier measles monoclonal antibody candidate, VMS063.
  • Replimune Group (NASDAQ: REPL) shares tumbled 19.5% from $5.91 to $4.76 Friday after the developer of oncolytic immunotherapies disclosed that the FDA for a second time had rejected the company’s biologics license application (BLA) for its lead product candidate RP1 (vusolimogene oderparepvec) in combination with nivolumab to treat advanced melanoma, instead issuing a complete response letter (CRL). Replimune criticized the FDA for an inconsistent review process, saying the agency contradicted earlier guidance to the company and assessed the resubmitted BLA through a different review team that replaced the team that previously interacted with the company. Replimune also defended the combination therapy’s data in the Phase II IGNYTE trial (NCT03767348)—a 34% response rate with a median duration of 24.8 months and a favorable safety profile, the basis of the combo’s breakthrough therapy designation. “We have no choice but to eliminate jobs, including substantially scaling back our U.S.-based manufacturing operations,” stated Replimune CEO Sushil Patel, PhD. Nivolumab is the cancer immunotherapy marketed as Opdivo® by Bristol Myers Squibb (NYSE: BMY).

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Mustafa Suleyman: AI development won’t hit a wall anytime soon—here’s why

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, from 312 teraflops in 2020 to 2,250 teraflops today. Our own Maia 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 like NVLink and InfiniBand 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 from Epoch 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 grown 5x 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 additional 200 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.

Mustafa Suleyman is CEO of Microsoft AI.

CRISPR at 25: The Past, Present, and Future of Genome Editing


Panelists:

Image of Rodolphe Barrangou, PhD

Rodolphe Barrangou, PhD

North Carolina State University

Panelist

Image of Rodolphe Barrangou, PhD

Rodolphe Barrangou, PhD

Rodolphe Barrangou, PhD, is the T. R. Klaenhammer Distinguished Professor at North Carolina State University, where he leads the CRISPR Lab. Rodolphe spent nine years at Danisco and DuPont, where he made seminal contributions in the functional characterization of CRISPR as a microbial immune system. He has been at NC State since 2013.

For his CRISPR work, Rodolphe has received several international awards, notably the Canada Gairdner International Award, and has been elected to the National Academy of Sciences, the National Academy of Engineering, and the National Academy of Inventors. Rodolphe is a scientific co-founder of Intellia Therapeutics, Locus Biosciences, TreeCo, Ancilia Biosciences, and CRISPR Biotechnologies, and an advisor to Inari and the IGI. He is also the founding Editor in Chief of The CRISPR Journal (published by Mary Ann Liebert, Inc., a Sage partner), which launched in 2018.

Rodolphe holds a degree from Paris Descartes University and a PhD in functional genomics from NC State.



Broadcast Date: 

  • Time: 

It has been almost 25 years since the acronym “CRISPR” was first coined. Since then, CRISPR has become a household word, a star of books and films, and a Nobel Prize–winning discovery. This powerful and disruptive genome editing technology has transformed countless fields, including gene therapy, xenotransplantation, de-extinction and agbiotech. Researchers continue to build on the CRISPR chassis, devising new platforms for bespoke genome editing. But major questions remain around clinical safety, commercial development, ethical deployment, and regulatory oversight.

In the first of a new series of GEN Keynote Webinars, Professor Rodolphe Barrangou, PhD (North Carolina State; EIC, The CRISPR Journal) offers a front-row perspective of the CRISPR revolution, the seminal advances, clinical highlights, and rising applications. Almost two decades ago, Barrangou provided the first experimental demonstration of the functional role of CRISPR. With numerous advisory and entrepreneurial activities in the gene editing space, Barrangou is the ideal guide to discuss CRISPR’s progress in the clinic; the state of the CRISPR toolbox; and the regulatory roadblocks and ethical challenges that will shape the application of CRISPR in agbiotech, germline editing, and other arenas.

Registration for this GEN Keynote Webinar is free. Following this live presentation, Dr. Barrangou will answer audience questions.

Produced with support from:

Elevate bio logo

The post CRISPR at 25: The Past, Present, and Future of Genome Editing appeared first on GEN – Genetic Engineering and Biotechnology News.

Igyxos Biotherapeutics is Enhancing Hormone Activity to Treat Infertility

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
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.

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