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Raphael Thys
The development of longevity treatments is hampered by a lack of biomarkers and validated drug targets. BioAge’s co-founder and CEO, Kristen Fortney, explains how the firm is enlisting machine learning (ML), artificial intelligence (AI) and biobanks to fill in the gaps.
The quest for human longevity treatments is attracting big cash in the biotech industry. One of the most impressive investments entering the field was the $3 billion launch of the U.S. anti-aging company Altos Labs in January 2022.
However, efforts to extend our healthy lifespan are dogged by a lack of clear biomarkers that correlate with the aging process. There are a set of observed hallmarks of aging, such as the breakdown of cells, a lack of stem cells in tissues and unstable DNA, but the search for reliable drug targets to slow the aging process is difficult.
In 2020, for example, the U.S. company Unity Biotechnology hit a major setback when its lead drug targeting aged, or “senescent,” cells failed to treat the age-related condition osteoarthritis in a phase 2 trial.
To overcome the challenges of developing longevity treatments, BioAge was launched in the U.S. in 2015. The firm raised a $90 million Series C round in late 2020 to finance the development of small molecule drug candidates for age-related conditions including anemia, muscle atrophy and COVID-19. BioAge also paired up with Age Lab AS in August 2022 to tap into the latter firm’s biobank — containing tissue samples collected from healthy humans over many years.
In an interview, BioAge’s co-founder and CEO, Kristen Fortney, told us how ML and AI in addition to biobank sampling can fuel the search for drug targets in longevity treatment.
Why is it a big challenge to find biomarkers for aging, and how is BioAge working to solve the issue?
Aging is a multifaceted process involving multiple mechanisms throughout the body. Consequently, the aging process is associated with thousands of changes in the proteome, transcriptome and metabolome. The critical challenge is determining which of these changes are functionally and clinically relevant. That knowledge, in turn, points the way toward the most promising drug targets to treat diseases of aging.
BioAge has exclusive access to biobanks containing samples collected longitudinally from thousands of individuals over up to 45 years of lifespan. Because these samples are linked to health records detailing medical outcomes of the donors, including lifespan, we can apply AI/ML techniques to omics data derived from the samples to identify the biological changes that are predictive of healthy longevity. Key proteins in the pathways involved then become candidate targets for our drug development programs.
Using this approach, our target discovery platform has powered novel insights into how we age, and identified actionable targets related to muscle, brain and immune aging.
One common issue in longevity research is that preclinical drug development in animal models sometimes doesn’t translate well to humans. How is BioAge addressing this problem?
We take two different strategies to de-risk our clinical programs. First, we start by examining signals in human longevity data, and focusing on pathways that have a powerful longevity association in our human cohorts. Second, every program that we advance has been validated in translational animal models using naturally aged mice. Not every disease model is equally predictive of clinical success — some are much more reliable than others. At BioAge we focus on tissues and processes that are more conserved between mouse and human.
How does BioAge’s strategy differ from other firms in the anti-aging space such as Altos Labs and Calico?
The defining feature of BioAge’s strategy is our human-first approach: We begin with human longevity data, ensuring that the targets we discover will be relevant to human aging. In addition, we analyze our human data in a mechanistically unbiased way: we’re not imposing prior beliefs about which pathways are most important, so we’re not limited in terms of what we can discover about the key molecular events underlying healthy longevity.
The other important aspect of our strategy is that because we’re studying human longevity data at scale using modern omics and AI, our analyses lead us to work on multiple different mechanisms, rather than focusing on a single target.
AI in drug discovery is still at an early stage. Can you tell me how the tool could change the way we search for aging-related targets and when this approach might be validated?
The AI/ML target discovery methods we’ve developed at BioAge have already dramatically accelerated our identification of age-related targets, illustrating how AI can revolutionize target discovery. Several of these targets have advanced through preclinical validation to the clinical stage.
In conventional approaches, the search for aging-related targets is limited by mechanistic bias: one needs to identify interventions or genetic differences that extend lifespan or affect age-related disease in model organisms, and then investigate the pathways involved. It’s necessarily a narrow approach.
By contrast, our use of AI enables us to analyze longevity data at scale, in an unbiased way, to identify targets involved in multiple mechanisms underlying healthy aging. Using omics technologies, we can characterize the levels of thousands of biomolecules in each sample from our cohorts, yielding millions of data points — far beyond the scale at which human analysts can make meaningful inferences. Our ML methods then detect patterns in these vast data that reveal the pathways whose activity levels are most strongly predictive of future health.
Because the analyses are not informed by prior hypotheses, we have the potential to capture many key mechanisms underlying healthy longevity, including those that have not yet been studied in animal models.
To give a couple of examples:
Our platform revealed that higher levels of apelin, a small circulating peptide, are associated with improved muscle function, reduced frailty and longer lifespan, suggesting that boosting the apelin pathway could be used therapeutically for indications related to muscle aging. We have promising preclinical data in muscle atrophy and regeneration models in mice, and we currently have a phase 1b trial ongoing of our drug BGE-105, which activates the apelin receptor APJ.
The BioAge platform also revealed that levels of NLRP3, a component of the inflammasome, rise with age and positively correlate with cognitive decline and all-cause mortality. We have created a series of novel central nervous system (CNS)-penetrant inhibitors of NLRP3 that we are developing to treat diseases driven by pathologic inflammation in the central nervous system, eye and peripheral organs.
Can you tell me the status of your phase 2-stage COVID-19 treatment program, and when we can expect topline results?
We don’t have any updates to share at present on that program, but we expect to make an announcement in the first half of next year.
How have investor attitudes changed towards the anti-aging space in past years, and how do you see the field changing in the next ten years?
In recent years, there has been a huge uptick in investor interest in the longevity biotech sector, but this is really just the beginning. When we look at fields like oncology, it’s clear that there’s so much room for additional growth, and once the first longevity-based treatment becomes an approved medicine, we will enter a new phase that could dwarf current levels of investment.
In the next decade, we’re going to see the first longevity-based treatments entering the clinic, and this will spark a revolution in healthcare as we shift from treating the symptoms of disease to addressing their root cause. The medications will initially be approved for narrow indications, but as the preventive potential of the drugs becomes clear, label expansion will make them available to more and more patients.
The broad adoption of medicines from the longevity sector will decrease the number of manifestations of aging that are considered inevitable aspects of growing older. As we move toward prevention, the onset of many diseases of aging will be delayed or even eliminated. Our ultimate vision is a world in which the process of growing older is uncoupled from declining health and loss of independence, allowing everyone to live longer and healthier lives.