As a postdoctoral researcher at Stanford University, Kristen Fortney used bioinformatics to check the genetics of supercentenarians — people who live to the age of 110 and beyond. Now she is on the forefront of biotech efforts to show longevity science knowledge into medicine. As CEO and co-founder of BioAge, a clinical stage biotech developing a pipeline of treatments to increase healthy lifespan by targeting molecular causes of aging, Fortney is working directly on a biological challenge that has attracted a number of the biggest minds, and deepest pockets, on the planet.
There’s an extended history of rich people directing their financial resources where they’ll make the best 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 numerous philanthropic efforts dedicated to cancer research. BioAge investors include Andreessen Horowitz, Redpoint, AARP Foundation, Kaiser Foundation Hospitals and Khosla Ventures.
Fortney says to deal with the best number of individuals through medical innovation, aging is goal. What’s more, aging biology is a singular lever point to delay the incidence of multiple diseases directly, and longevity science has arrived at the purpose where it is prepared to begin translating knowledge into therapies.
The next interview, conducted via phone and email, has been edited for length and clarity.
CNBC: VCs and pharmaceuticals are beginning to pay more attention to the science of longevity. Why the sudden shift in interest?
Fortney: Aging is the first reason for many chronic diseases, including devastating illnesses like cancers and Alzheimer’s. We have known that for a very long time, but in recent times, science has advanced to the purpose that we’re confident we are able to do something about it. Researchers have discovered multiple interventions that may increase healthy longevity in animal models, showing that healthspan will be prolonged. At the identical time, technological progress has given us unprecedented understanding of human aging, in addition to the power to translate this data into therapeutics. Targeting aging will enable us to treat disease in entirely recent ways. As awareness of that potential grows, it’s attracting intense interest within 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 concerning 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 of their 50s and 60s, whereas others live into their 90s and beyond in good health. We use AI and machine learning to research the distinctive molecular features of individuals who live the healthiest, longest lives, after which use that knowledge to develop therapies that might help everyone age more successfully. Because we’re using modern technologies to get a comprehensive molecular picture of aging, we’re capable of discover many alternative aging mechanisms, quite than being limited to a handful of targets chosen prematurely.
CNBC: Tell us how you incorporate AI into your drug pipeline.
Fortney: AI and ML are the important thing 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 1000’s of individuals over a long time — coupled with detailed records of their health and mortality, which we access through exclusive partnerships with unique biobanks all over the world.
We analyze each sample using modern omics technologies, measuring tens of 1000’s of proteins, RNAs, and metabolites. The resultant datasets are huge and complicated, so we use modern AI and statistical techniques to sift through the subtle patterns and discover the biological pathways and molecular aspects underlying healthy longevity. Ultimately, we’re on the lookout for the pathways that distinguish probably the most successful agers. The proteins that play key roles in these pathways turn into our drug targets.
CNBC: How might BioAge’s approach to developing therapeutics decelerate or prevent age-related disorders?
Fortney: Because aging drives disease, targets which are related to aging will help combat disease. A central aspect of our approach is discovering pathways which, after they’re lively in certain ways, end in a healthier person. So, drugs geared toward these mechanisms have the potential to be curative for some diseases and in addition slow or prevent them.
Within the near term, we’re developing drugs to treat specific diseases, but in the long term, we’re envisioning a path just like what happened with the statins. They were originally approved for a narrow indication, familial hypercholesterolemia, but over time they were applied an increasing number of broadly, and today they’re used widely in mainly healthy people to stop heart problems. Our muscle aging drug that we recently tested in a successful Phase 1 trial is an amazing example of a clinical program that might follow an identical path.
CNBC: How do you apply machine learning methodology into studies?
Fortney: We consider in learning as much as possible from our clinical trials, not only concerning the primary indication but additionally about aging itself. We achieve this using biomarkers that we construct with our machine learning approach. Let me give an example: Our ML evaluation 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 capable of study biomarkers that correlate with long-term functional impacts over a long time. This shows the facility of our ML methods to disclose recent aging biology and ensure that our drugs are exerting useful effects on the aging process.
CNBC: Tell us more about BioAge’s clinical programs on muscle aging.
Fortney: We recently accomplished a successful Phase 1b trial of the lead drug in our muscle aging program. The drug, BGE-105, mimics the results of apelin, a small peptide that plays essential roles in muscle regeneration. Our aging cohorts revealed that folks with higher activity within the apelin pathway lived longer and maintained higher muscle and cognitive function as they aged. Within the trial, BGE-105 prevented muscle atrophy in people over 65, and this has implications for numerous medical conditions with high unmet need.
We’re now moving forward with multiple Phase 2 trials of this drug, one to stop severe muscle atrophy in ICU patients, and one other to combat muscle loss in patients being treated for obesity. Over the long term, we would like to go after sarcopenia [age-related loss of skeletal muscle mass and strength] itself.
CNBC: Why is the prevention of muscle atrophy so essential? And to this point, no therapies have been discovered to stop muscle aging, correct?
Muscle atrophy decreases mobility, robbing older people of their autonomy and dignity, and sometimes forcing them into nursing homes. As well as, declining muscle function compounds the danger of falls, that are a significant reason for accidental death in older people. Frailty affects 15% of the population over 65, greater than 8 million people within the U.S. alone, and some extent of muscle atrophy is a virtually universal aspect of aging. But despite its prevalence, we’ve no effective treatment, so that is an infinite unmet medical need that we hope to deal with with our clinical programs.
CNBC: You employ proprietary human samples with detailed health records. Explain how you utilize this to map out molecular pathways.
Fortney: Along with biological samples, our biobanks also contain wealthy health data, not only how long the donors lived and after they got sick, but functional measures relevant to on a regular basis life. Using these two kinds of data together, we are able to interrogate the molecular profiles generated by our omics analyses and discover the sorts of changes that predict, for instance, a discount in grip strength or declining cognition. This approach gives us the unique ability to attach 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 virtually universal aspect of the aging process, and might 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 essential roles in brain aging. For instance, 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 elements of brain aging and treat and even prevent age-related neurological diseases.
CNBC: Are you able to explain more about your focus on NLRP3 inhibitors in brain aging, what they’re and what progress you could have made using AI data.
Fortney: Like many aging targets, NLRP3 is on the nexus of multiple disease processes. Chronic activation of the NLRP3 inflammasome with age contributes to pathologic inflammation, driving disorders within the brain in addition to 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 discover a recent class of molecules that may just do that.
A very exciting feature of our recent NLRP3 inhibitors is that a few of them can cross the blood-brain barrier and are due to this fact suitable for applications related to brain aging. Other molecules on this recent class of inhibitors will probably be used for targeting inflammation in the attention 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 within the trial for our muscle atrophy drug, we’ll leverage the biomarkers to learn as much as possible concerning the aging process in parallel with the first endpoints of the studies.
CNBC: What key partnerships do you could have and what are you doing together?
Fortney: Human data and human samples are central to every thing we do at BioAge. Nevertheless, human aging takes an extended time, so we’d like some approach to follow aging in individuals that does not require us to attend 80 years to gather our data. We solved this problem by establishing exclusive partnerships with multiple aging biobanks, which contain samples collected longitudinally from healthy people over a long time of follow-up. These resources provide invaluable insight into the molecular bases of healthy longevity.
For instance, last 12 months we announced a partnership with Age Labs, a diagnostic company based in Norway, that permits us to research an enormous biobank collected from greater than 100,000 volunteers over greater than 25 years of aging. The information generated by the partnership will dramatically speed up our ability to find recent aging mechanisms and to discover, develop, and commercialize drug targets for age-related disease. The Age Labs collaboration is just considered one of several lively partnerships, and more news on this area is coming soon.
CNBC: Where do the investable opportunities lie? Unlocking ways to stop diseases? Or sub-investable areas like geroscience, age-tech, regenerative medicine, longevity fintech, longevity fem-tech?
Fortney: It is important to keep in mind that we’re still within the early years of longevity biotech, and we expect that the variety of potential mechanisms in addition to applications to grow substantially over time. Within the near term, we consider that studying aging biology will give us recent drugs for diseases where there is a high unmet need. Over the long term, we’ll be unlocking ways to stop disease from arising in the primary place.
To return to the analogy I made earlier, the statin drugs evolved into what are essentially preventive medicines for heart disease, and we are able to envision medications based on aging biology which are eventually used to stop huge blockbuster diseases of aging with very high prevalence. Imagine a drug that might prevent muscle loss and thereby mainly eliminate frailty, or that might dramatically slow cognitive decline. The supply of medicine like that might revolutionize healthcare — not to say helping older people to live full, independent lives.