New financing backs a physics-based approach that aims to move geroscience from observing aging to predicting it.
For much of modern biology, aging has been treated as a phenomenon to describe. Researchers have cataloged hallmarks, mapped pathways and identified biomarkers; each discovery has added another piece to a puzzle that keeps adding pieces. Gero is pursuing a different question. What if the puzzle itself follows rules?
The Singapore- and San Francisco-based company has announced $17 million in new financing, bringing its total equity funding to $34 million. The raise follows Gero’s collaboration with Chugai Pharmaceutical, part of the Roche Group, which includes an upfront payment and up to $250 million in milestones plus royalties. Together, the funding and partnership support a platform built around a striking proposition: that aging and age-related disease progression can be understood through quantifiable physical laws embedded within large-scale human data.
Rather than focusing solely on reversing cellular damage or restoring youthful function, Gero aims to identify mechanisms that influence the rate of aging itself.
Longevity.Technology: Funding announcements are rarely about funding alone. In Gero’s case, the more interesting question is what investors believe they are actually backing. Beneath the $17 million raise sits a proposition that borders on the audacious: that aging is not merely a collection of biological mishaps accumulating over time, but a process governed by underlying principles that can be measured, modeled and, ultimately, manipulated. Biology has traditionally been regarded as gloriously complicated – a tangle of pathways, feedback loops and exceptions to every apparent rule. Physics, by contrast, progresses by identifying the rules themselves. Gero’s wager is that aging may belong closer to the latter category than the former.
That idea lands at an intriguing moment for geroscience. For much of the field’s history, the emphasis has been on observation – cataloguing hallmarks, pathways and biomarkers, assembling an ever more detailed inventory of how aging unfolds. Necessary work, certainly. Yet inventories are not explanations. The next phase may involve something more ambitious: moving from describing aging to modeling it, from identifying its signatures to understanding its dynamics. If that sounds suspiciously like turning biology into an engineering problem, that is because it is.
The naked mole-rat lurking in Gero’s origin story is a useful reminder that nature has already demonstrated that aging rates are not fixed. Evolution, inconveniently, has already run the experiment – and the naked mole-rat won. The challenge is no longer proving that slower aging is biologically achievable; it is cracking the rules that govern it. That investors and pharmaceutical partners are increasingly willing to fund that ambition is, at minimum, a signal that the field has grown tired of merely watching aging happen. We may be approaching a point where the field is no longer content merely to observe aging – it wants to model it.
From observation to prediction
At the center of Gero’s approach is a framework developed over more than a decade by cofounder and CEO Dr Peter Fedichev and collaborators including Dr Jan Gruber at the National University of Singapore. Drawing on concepts from statistical physics and dynamical systems theory, the work proposes that aging can be understood as a gradual loss of physiological resilience – a process that leaves detectable signatures within longitudinal health data.
That framework helped establish the field sometimes referred to as gerophysics, which applies mathematical and physical principles to aging biology. Rather than examining isolated molecular pathways, the goal is to identify system-level dynamics that govern how organisms lose stability over time.
To pursue that objective, Gero has trained AI models on approximately 10 million curated longitudinal medical records selected from a pool of more than 100 million records and integrated with molecular, omics and genetic datasets. The company argues that these models can distinguish reversible disease processes from deeper age-related mechanisms and identify shared biological drivers across multiple chronic conditions.
Searching for common causes
The attraction of such an approach is obvious. Aging sits upstream of cardiovascular disease, neurodegeneration, metabolic dysfunction and many other conditions that dominate healthcare spending and disability in later life. If common mechanisms exist, targeting them could potentially influence multiple diseases simultaneously.
That prospect has attracted attention from both academia and industry.
“Target selection is the critical bottleneck in translating aging biology into medicines, and the field has not lacked ideas, it has lacked human-evidence-grounded targets that pharma is ready to develop,” said Brian K Kennedy, Distinguished Professor at the National University of Singapore and independent director of Gero.
“Gero is one of the few groups bringing published aging theory, longitudinal human data, and pharmaceutical-partner validation into a single discovery engine.”
The company’s collaboration with Chugai reflects that interest. Under the agreement, Chugai is advancing drug candidates against targets identified through Gero’s platform, providing one of the clearest tests yet of whether physics-inspired aging models can generate clinically relevant therapeutic hypotheses.
A different kind of AI company
AI drug discovery has become a crowded category, but Gero’s positioning differs from many competitors. The company is not primarily arguing that artificial intelligence can search biological space faster. Instead, it contends that AI can help uncover the mathematical structure underlying aging itself.
That distinction appears to have resonated with investors.
“Gero stands out for grounding discovery in a published, physics-based framework and real human data, and for turning that science into a working platform, which is why I invested,” said Anita Cosgrove, former Senior Vice-President of Strategic Business Development at Human Longevity, Inc and an investor in the company.
The financing included participation from technology and pharmaceutical sector investors, reflecting the increasingly blurred boundary between computational science and biomedical research.
Where the models lead
The next few years may reveal whether the field is prepared to embrace aging as a problem of prediction as much as intervention. Gero’s platform ultimately rises or falls on its ability to generate targets that translate into medicines and, eventually, measurable benefits for patients.
Fedichev is clear about what would constitute proof that the company’s framework is capturing something fundamental about aging rather than simply identifying patterns in large datasets.
“Ultimate proof can only come from prospective experiments with therapeutic interventions,” he told Longevity.Technology. “We are already running the first such studies and expect clear, definitive results in the coming years.”
The emphasis on intervention exposes one of geroscience’s more awkward structural problems. Aging unfolds slowly – most physiological decline progresses at a pace that a standard clinical trial is poorly designed to capture. If you cannot wait decades for a lifespan readout, you need biomarkers precise enough to detect that something fundamental is shifting; and those biomarkers, for the most part, do not yet exist in validated form.
“The intrinsic rate of aging in humans is very slow – most functional declines occur at roughly 1% per year,” said Fedichev. “That is why robust biomarkers of aging are so important.”
He argues that the field will need tools that move beyond conventional biological age clocks. “In our most recent work, we pitch novel gerophysics-motivated biomarkers that go beyond conventional aging clocks and are better suited for aging biology in long-lived species.”
Interestingly, Fedichev does not expect the first generation of aging-directed therapies to produce dramatic changes in day-to-day function. Instead, he believes the earliest signal may be a reduction in disease burden and an extension of healthy survival toward the limits of human biology.
“In the near term, we aim to demonstrate interventions that push average human lifespan toward its biological maximum,” he told us. “We are talking about pushing lifespan within an 80 to 120 years range. Such therapies would dramatically reduce disease burden while leaving the gradual functional decline largely untouched – which makes them far more practical for initial clinical trials. Only later will we run trials that directly target and slow the rate of aging itself.”
Biology has spent decades cataloguing the components of aging; companies such as Gero are asking whether we can now begin to understand the rules that connect them. If the answer proves to be yes, the consequences could extend far beyond a single platform – reshaping how the field thinks about aging itself.
