The Big Picture
Let's call the impact of biomedicine a Life Dividend, quantified as the rise in life expectancy per year. Current biomedical research (with ~no contribution from the Longevity field yet) 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.
To understand and improve progress in adding years of healthy life, we focus not on individual breakthroughs or medicines but on the broader system that produces these.
Timeline from Basic Research to Human Proof
In order to add years of health for people, new research must be developed into a medicine and then go through human testing. This creates an Interval of several years before the Dividend from a new breakthrough arrives. And many ideas fail at some point of the path.
The Dividend in any given year arises from the number of ideas (Ideast−k) that have had time to go through the Interval (tlag), times the probability of attempting to make a new medicine from the idea (p(Attempt)), times the probability of success (p(Success)), times the number of healthy years added by the new medicine (QALYs).
Where is Progress coming from?
Norn Group's work focuses on addressing factors limiting throughput and impact of medicines at both Research, Development, and Human Testing stages. The following sections will explore each of the three stages in more detail.
FAQs
How should I use this page?
We've divided the big picture into three stages, matching different sets of expertise at making medicines. Within each we group factors affecting progress (represented by circular nodes) into classes. Click on the nodes or the class label to read about ongoing work and opportunities.
How can I contribute?
Look for two icons: Question marks indicate gaps not being addressed (good topics to think about if you want to join Nexus, while the Norn symbol denotes proposals or ongoing efforts along with what's needed to move them forward (please get in touch!)
How is this different than other tech trees/roadmaps?
We don't try to define an exact sequence of research (premature, given that we don't yet know how aging works), but rather a system that's able to consistently deliver the health impact we need.
Human Testing
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. It is rare that there's a mandate to experiment with something new, and few non-profit organizations can afford the cost of trials. As long as this remains true, medicines from the longevity field will most often be channeled through trials for individual diseases.
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.
Improving Impact
The QALYs, probability of success, and crowding for each trial are largely determined at earlier stages of Research & Development, and can best be optimized there.
Improving Iterations
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). Any drug intended to prevent progressive disease outcomes means either a long and large trial, or a validated surrogate endpoint. A surrogate endpoint would reduce the time and cost of many/most trials for age-related disease at least two-fold.
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.
The highest leverage in Human Testing is to find ways of running aging trials faster and cheaper (e.g. with biomarkers), or to generate robust data for longevity medicines without traditional clinical trials (e.g. in pets, transplant organs, or direct-to-consumer settings).
Currently, trials based on preventing disease will need five to ten years each, and cost at least tens of millions. Reversing disease incentivizes drugs for specific conditions rather than broader health.
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.
In order to receive significant funding for trials, longevity medicine needs to achieve a similar (perceived) probability of 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'. Either way, establishing and demonstrating measures with real predictive power is the best way to enable clinical trials to happen.
Predictive power for clinical outcomes is the highest leverage at the Development stage. Right now, there’s no convincing estimate the longevity field can point to.
Absent a good estimate of success rates, it’s easy for people to assume they will be zero. Conversely, if we show that success rates of 5% are likely, longevity medicines would be in the same category as neurodegeneration. Such an estimate could come from a combination of improving the rigor of preclinical tests, and comparing these to human health data.
[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:Norn’s rough estimate of the longevity field’s hypothesis output rate is 2-4 per year.
This might yield ~0.1 years of life expectancy annually starting two decades from now, if the rate of successful translation is 1% and we get 3 years of healthy life per success.
To increase output we propose funding a diverse set of ideas based on both potential impact and potential to bring new perspectives on aging biology. 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 some of these are yet to be discovered.
Hypotheses will remain a bottleneck until output is increased at least an order of magnitude. We highlight key factors affecting these output below, with some perspective on opportunities for improving each one.
Where To Focus
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 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.
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.
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 Hypothesis Output Rate increases it becomes more important to improve cost/time in Development.