We’re a 501c3 nonprofit conducting and supporting research to help people live better and longer lives.
The plan to achieve our mission and its associated initiatives are focused on concrete outcomes:
The Impact Interval:
Timeline from Basic Research to Human Proof
In order to add years of health for people, a new idea must be translated into a medicine and then tested in humans. This creates an Impact Interval of several years, before the Life Dividend of a new breakthrough arrives. And many ideas fail at some point of the path.
In a given year, the Dividend arises from the number of ideas that have passed the Interval period, times the probability that each one is put through the path, times the probability of success, times the number of healthy years added by the new medicine.
Where is Progress coming from?
The Life Dividend can be quantified as the rise in life expectancy per year. Current biomedical research (with ~no contribution from the Longevity field) is yielding 0.1-0.2 yr/yr by targeting individual diseases and improving public health.
The Longevity field aims to increase the Dividend, mainly by providing medicines with dramatically higher 'healthy years' impact. The goal is to reach a Dividend of >1 yr/yr.
Parts of the field, Norn included, also seek to develop medicines that can continue to yield this dividend many years into the future, by treating each underlying driver of decline.
Our work focuses on addressing factors limiting throughput and impact of medicines at both Research, Development, and Human Testing stages.
Human Testing
The productivity of this stage can be described by positive impact (QALYs times probability of success times a 'Crowding factor' when many similar treatments are being pursued) divided by Cost and Interval factors.
Cost and Interval limit how many medicines can be tested in this stage. Making trials faster and/or cheaper to run will increase throughput (if not bottlenecked at Development stage).
Throughout clinical trials, companies are spending large amounts of $ in culminating many years of work. The cost of failure is high, and focus is on avoiding risk.
In order to receive significant funding for trials, longevity medicine needs to achieve a similar perceived p(Success) as 'normal' medicines.
This may initially come from medicines targeted at a subset of diseases ('multimorbidity medicines'), like GLP1R agonists, rather than targeting 'aging'.
Improving Impact
The QALYs, p(Success), and Crowding, in each trial are largely determined at earlier stages of the path, and can be optimized there.
Improving Interval
Any drug intended to prevent progressive disease outcomes means either a long and large trial, or a validated surrogate endpoint. Many/most trials for age-related disease would gain multiple years (and lots of $) with a surrogate endpoint.
Making and qualifying a surrogate is an extensive process, first generating supporting data, then showing predictive power (in trials), then the FDA qualification process.
With no success story yet, the Longevity field benefits even more from faster/cheaper trials, both because investment is limited by uncertain p(Success) and because we need to experiment to find effective endpoints and trial designs.
We believe this is the highest leverage at Human Testing stage.
Development
Pharma strategy and investor preference determine which therapeutic areas are pursued. This depends on perceived risks (including lack of clear path) as well as potential.
[Explore the three strands.]
Research
Many factors influence productivity at the research stage, while also influencing each other. For example, tools and funding both help talented researchers develop strong hypotheses, and those hypotheses in turn attract more funding.
Rather than building a quantitative model with multiple hard-to-quantify factors, we propose two key readouts:We highlight key factors affecting these readouts below, with some perspective on opportunities for improving each one.
At the present maturity of understanding the aging process, we do not think it fruitful to look for 'the best approach/research topic'. It is very likely that multiple approaches will be important, and that many of these are yet to be discovered.
Instead, we propose funding a diverse set of ideas based both potential impact and potential to bring new perspectives on aging biology.
How can we accelerate development?
New hypotheses from Research as input to Development are a bottleneck, but codependent with our ability to assess and advance them to humans.
Currently hypotheses are the limiting factor, but if the Output Rate increases it becomes more important to improve both Predictive Validity and Cost/Time in Development.
The Big Picture
Improving productivity at each stage increases our annual Life Dividend. And indications of success at later stages also feeds confidence and information back to earlier stages.
The first success of a longevity medicine in humans (or perhaps pets) will draw a lot of additional funding and talent, similar to the obesity field after GLP1R agonist successes. The more real attempts we can make, the faster this will happen.
Demonstrating and improving Predictive Validity during Development can be a leading indicator of human p(Success), and also provides feedback to Research about the promise of different approaches.
Funding a broad suite of new ideas and attracting talented researchers is a gradual process, and should be continued without pause to ensure a wellspring of hypotheses.
Some improvements will take longer to implement, and/or will have greater Intervals before affecting our productivity. For example, a new idea must go through each stage, while improving predictions may require data collected at later stages.When an improvement has high impact and a decade or more to Impact, we should prioritize effort and investment now.
Produced for the Talent Bridge Award : This piece dives into the real bottleneck aging biomarkers face: our data. It maps the missing datasets, outlines what’s actually needed for biomarker validation, and proposes a path forward—designed to help researchers, biotech, and funders unlock real-world impact in aging therapeutics.
AAV gene therapy costs are rapidly dropping, echoing antibody manufacturing’s 15–20-year trajectory. Discover how scaled production, better safety data, and innovative delivery strategies open the door to treating common diseases like Parkinson’s, arthritis, and more.
A video primer presentation by Martin Borch Jensen built for people new to the field of aging biology. During this presentation Martin defines aging and its associated problems, the various biological mechanisms of aging, and current challenges plaguing the field.
The journal club goes through landmark discoveries and topics in aging biology, with a focus on primary research papers. It’s intended for people who have gained familiarity with topics within aging biology and want to refine their understanding of what’s possible and what’s next. Content includes links to papers, slides from presenters, and notes from our discussions. Chronologically going through these topics highlights how young the field is (a couple of decades), and how even major conference topics often arose from a single bold experiment. We read between the lines to evaluate strong claims, deduce limitations, and discuss implications.
Created in conjunction with How to Design a Clinical Trial in Aging . A tool to estimate trial sample size requirements while designing clinical trials targeting aging. It models the feasibility of multimorbidity prevention trials by integrating hazard ratios and incidence rates across multiple age-related conditions, and helps determine global effect size estimates to justify a cost-effective trial design.
Created through our Apprenticeship Program, this overview includes data on incidence, etiology, clinical trials, animal models, and more. Designed to reduce duplications of efforts from longevity biotechnology companies while choosing disease(s) to test drug candidate efficacy.