A longevity expert who studied people who live to 110 on how humanity and AI will master aging

As a postdoctoral researcher at Stanford University, Kristen Fortney used bioinformatics to study the genetics of supercentenarians — people who live to the age of 110 and beyond. Now she is at the forefront of biotech efforts to turn longevity science knowledge into medicine. As CEO and co-founder of BioAge, a clinical stage biotech developing a pipeline of treatments to extend healthy lifespan by targeting molecular causes of aging, Fortney is working directly on a biological challenge that has attracted some of the biggest minds, and deepest pockets, in the world.

There’s a long history of wealthy people directing their financial resources where they can make the greatest positive impact on human health, she noted in a recent interview with CNBC ahead of its upcoming Healthy Returns virtual conference. Examples include the Chan-Zuckerberg Initiative, the Broad Institute, the Paul Allen Institute for Brain Science, and the many philanthropic efforts devoted to cancer research. BioAge investors include Andreessen Horowitz, Redpoint, AARP Foundation, Kaiser Foundation Hospitals and Khosla Ventures.

Fortney says to address the greatest number of people through medical innovation, aging is a good target. What’s more, aging biology is a unique lever point to delay the incidence of multiple diseases at once, and longevity science has arrived at the point where it is ready to start translating knowledge into therapies.

The following interview, conducted via phone and email, has been edited for length and clarity.

CNBC: VCs and pharmaceuticals are starting to pay more attention to the science of longevity. Why the sudden shift in interest?

Fortney: Aging is the primary cause of many chronic diseases, including devastating illnesses like cancers and Alzheimer’s. We’ve known that for a long time, but in recent years, science has advanced to the point that we’re confident we can do something about it. Researchers have discovered multiple interventions that can increase healthy longevity in animal models, showing that healthspan can be extended. At the same time, technological progress has given us unprecedented understanding of human aging, as well as the ability to translate this knowledge into therapeutics. Targeting aging will enable us to treat disease in entirely new ways. As awareness of that potential grows, it is attracting intense interest in the sector.

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CNBC: What area of longevity is your startup focused on?

Fortney: BioAge takes a “human data first” approach to understanding aging, learning about the underlying mechanisms of healthy longevity from humans who are already aging well. People age at different rates — some die of an age-related disease in their 50s and 60s, whereas others live into their 90s and beyond in good health. We use AI and machine learning to analyze the distinctive molecular features of people who live the healthiest, longest lives, and then use that knowledge to develop therapies that could help everyone age more successfully. Because we’re using modern technologies to get a comprehensive molecular picture of aging, we are able to discover many different aging mechanisms, rather than being limited to a handful of targets chosen in advance.

CNBC: Tell us how you incorporate AI into your drug pipeline.

Fortney: AI and ML are the key technologies that enable us to pinpoint the molecular differences that predict healthy versus unhealthy aging. Our discovery process begins with our aging cohorts — precious samples collected from thousands of people over decades — coupled with detailed records of their health and mortality, which we access through exclusive partnerships with unique biobanks around the world.

We analyze each sample using modern omics technologies, measuring tens of thousands of proteins, RNAs, and metabolites. The resultant datasets are huge and complex, so we use modern AI and statistical techniques to sift through the subtle patterns and identify the biological pathways and molecular factors underlying healthy longevity. Ultimately, we’re looking for the pathways that distinguish the most successful agers. The proteins that play key roles in these pathways become our drug targets.

CNBC: How might BioAge’s approach to developing therapeutics slow down or prevent age-related disorders?

Fortney: Because aging drives disease, targets that are related to aging will help combat disease. A central aspect of our approach is discovering pathways which, when they’re active in certain ways, result in a healthier person. So, drugs aimed at these mechanisms have the potential to be curative for some diseases and also slow or prevent them.

In the near term, we’re developing drugs to treat specific diseases, but in the longer term, we’re envisioning a path similar to what happened with the statins. They were originally approved for a narrow indication, familial hypercholesterolemia, but over time they were applied more and more broadly, and today they are used widely in basically healthy people to prevent cardiovascular disease. Our muscle aging drug that we recently tested in a successful Phase 1 trial is a great example of a clinical program that could follow a similar path.

CNBC: How do you apply machine learning methodology into studies?

Fortney: We believe in learning as much as possible from our clinical trials, not only about the primary indication but also about aging itself. We achieve this using biomarkers that we build with our machine learning approach. Let me give an example: Our ML analysis of our aging cohorts yielded biomarkers of long-term physical function — sets of proteins whose levels predict your future functional status, walking speed, grip strength, etc.

In a recent clinical trial for muscle atrophy, we showed that our drug triggered changes in these biomarkers that mirrored what we see in people who retain high levels of physical function throughout their lives. So even in a short-term study, we were able to learn about biomarkers that correlate with long-term functional impacts over decades. This shows the power of our ML methods to reveal new aging biology and confirm that our drugs are exerting beneficial effects on the aging process.

CNBC: Tell us more about BioAge’s clinical programs on muscle aging.

Fortney: We recently completed a successful Phase 1b trial of the lead drug in our muscle aging program. The drug, BGE-105, mimics the effects of apelin, a small peptide that plays important roles in muscle regeneration. Our aging cohorts revealed that people with higher activity in the apelin pathway lived longer and maintained better muscle and cognitive function as they aged. In the trial, BGE-105 prevented muscle atrophy in people over 65, and this has implications for a large number of medical conditions with high unmet need.

We’re now moving forward with multiple Phase 2 trials of this drug, one to prevent severe muscle atrophy in ICU patients, and another to combat muscle loss in patients being treated for obesity. Over the longer term, we want to go after sarcopenia [age-related loss of skeletal muscle mass and strength] itself.

CNBC: Why is the prevention of muscle atrophy so important? And so far, no therapies have been discovered to prevent muscle aging, correct?

Muscle atrophy decreases mobility, robbing older people of their autonomy and dignity, and often forcing them into nursing homes. In addition, declining muscle function compounds the risk of falls, which are a major cause of accidental death in older people. Frailty affects 15% of the population over 65, more than 8 million people in the U.S. alone, and some degree of muscle atrophy is a nearly universal aspect of aging. But despite its prevalence, we have no effective treatment, so this is an enormous unmet medical need that we are hoping to address with our clinical programs.

CNBC: You use proprietary human samples with detailed health records. Explain how you use this to map out molecular pathways.

Fortney: In addition to biological samples, our biobanks also contain rich health data, not just how long the donors lived and when they got sick, but functional measures relevant to everyday life. Using these two kinds of information together, we can interrogate the molecular profiles generated by our omics analyses and identify the kinds of changes that predict, for example, a reduction in grip strength or declining cognition. This approach gives us the unique ability to connect molecular pathways to health and disease.

CNBC: Turning to brain aging, what do your studies show on this front?

Fortney: Like muscle loss, cognitive decline is a nearly universal aspect of the aging process, and can range in severity from mild memory impairment to severe illnesses like Alzheimer’s. Our ML analyses of our aging cohorts revealed multiple pathways that play important roles in brain aging. For example, higher activity of a cellular machine called the NLRP3 inflammasome was correlated with more rapid decline in cognitive function with age. This implied that if we could decrease inflammasome activity, we could slow some aspects of brain aging and treat or even prevent age-related neurological diseases.

CNBC: Can you explain more about your focus on NLRP3 inhibitors in brain aging, what they are and what progress you have made using AI data.

Fortney: Like many aging targets, NLRP3 is at the nexus of multiple disease processes. Chronic activation of the NLRP3 inflammasome with age contributes to pathologic inflammation, driving disorders in the brain as well as the peripheral tissues. We reasoned that if we could inhibit the activity of NLRP3, we could bring that age-related inflammation under control, so we screened through billions of compounds to identify a new class of molecules that can do just that.

A particularly exciting feature of our new NLRP3 inhibitors is that some of them can cross the blood-brain barrier and are therefore suitable for applications related to brain aging. Other molecules in this new class of inhibitors will be used for targeting inflammation in the eye or other tissues outside the nervous system.

When these drugs are ready for clinical trial, the biomarkers related to cognitive function that we built using our AI platform will help us with patient selection and assessing the drug’s effects — and as in the trial for our muscle atrophy drug, we’ll leverage the biomarkers to learn as much as possible about the aging process in parallel with the primary endpoints of the studies.

CNBC: What key partnerships do you have and what are you doing together?

Fortney: Human data and human samples are central to everything we do at BioAge. However, human aging takes a long time, so we need some way to follow aging in individuals that doesn’t require us to wait 80 years to collect our data. We solved this problem by establishing exclusive partnerships with multiple aging biobanks, which contain samples collected longitudinally from healthy people over decades of follow-up. These resources provide invaluable insight into the molecular bases of healthy longevity.

For example, last year we announced a partnership with Age Labs, a diagnostic company based in Norway, that allows us to analyze a huge biobank collected from more than 100,000 volunteers over more than 25 years of aging. The data generated by the partnership will dramatically accelerate our ability to discover new aging mechanisms and to identify, develop, and commercialize drug targets for age-related disease. The Age Labs collaboration is just one of several active partnerships, and more news in this area is coming soon.

CNBC: Where do the investable opportunities lie? Unlocking ways to prevent diseases? Or sub-investable areas like geroscience, age-tech, regenerative medicine, longevity fintech, longevity fem-tech?

Fortney: It’s important to remember that we’re still in the early years of longevity biotech, and we expect that the number of potential mechanisms as well as applications to grow substantially over time. In the near term, we believe that studying aging biology will give us new drugs for diseases where there’s a high unmet need. Over the longer term, we’ll be unlocking ways to prevent disease from arising in the first place.

To go back to the analogy I made earlier, the statin drugs evolved into what are essentially preventive medicines for heart disease, and we can envision medications based on aging biology that are eventually used to prevent huge blockbuster diseases of aging with very high prevalence. Imagine a drug that could prevent muscle loss and thereby basically eliminate frailty, or that could dramatically slow cognitive decline. The availability of drugs like that could revolutionize healthcare — not to mention helping older people to live full, independent lives. 


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