In this session, Brain Kennedy from Buck Institute presented his assessment of the state of aging research in academia and the non incentivized academic research that could dramatically advance progress. After that, Lynne Cox introduced two progress opportunities she identified – new drug discovery approaches focusing on polypharmacology, and in silico systems modelling of aging. Following that was a last presentation from Joris Deelen, in which he presented ideas on how to get already discovered aging biomarkers into the clinic, as well as a novel approach to utilize genetics of long lived people to move the field forwards.
Presentation: Brain Kennedy
What is the lowest hanging fruit in your opinion?
- What do we mean when we say network regulating aging? How do we directly find out when we modulate a certain pathway, how do we primarily assign the role in aging? If it is just maintaining the network, what does it mean from a biological perspective? We see a lot of claims that this intervention affects inflammation, stem cells, etc., but what does it mean in terms of primary effects of this intervention and how does that get translated into the preservation of the network?
- Yeast research is great for finding limitations of waves of interventions: Especially for figuring out the limitations and waves of limitations that will need to be addressed after we are able to address the current limitations we are aware of with our current understanding (that is being translated now). This was revealed in earlier research in yeast – when we knocked out all the genes we thought affected lifespan positively and it worked, we ran into another set of different limitations in that yeast with prolonged lifespan. And we wouldn’t know about it if we didn’t conquer the first wave limitations first. It’s likely that there will be such a second wave of limitations and barriers in humans as well.
Key discoveries were made in academia that the private sector couldn’t fund:
- There are many more known unknowns and unknown unknowns. So we cannot forget that and should try to get more people working on basic science in aging, and let serendipity run its course instead of investing $100M specifically into Alzheimer’s research. More and more people are focusing on translational research, which is good on one side but there’s a risk on the other that we lose long-term discoveries that could have even more value.
Focus on commercialization might actually have an upside if done well:
- Hopefully fitting analogy is what happened with the computer science field – scientific discoveries led to the internet, which led to the dotcom boom, which led to more people going into academia and doing basic computer science research. There was an international effort to set up standardized tooling and an interoperability scaffold for the entire field, allowing all types of organizations – both public and private – to build on top of it – that should be the goal for the longevity field as well.
Presentation: Lynne Cox
To your knowledge what are best in silico models applicable to what you just said? (e.g. compbiomed.eu, Karr lab, Ideker lab,…)
- There are a few online models trying to build a virtual cell, but essentially I don’t think they’re based on enough biochemical data to make any sense. One is based in Germany, but it doesn’t model the complexities we know exist (where we can take one variable and see all the downstream effects, right now you can take one variable and you can read maybe one or two factors), which is necessary for progress. Another group is in Newcastle that is also doing this, but the complexity is just not there yet, probably due to the quality of material and data used for the modelling.
Are there areas where systems pharmacology is limited by tools, either for characterization of experimental intervention? That’s one way how science progresses – basic scientists say this is a known unknown, and something we would like to be able to measure or experimentally intervene, we can’t do it now, and then ask people in physical sciences or engineering to develop a tool with a set of performance characteristics.
- A tool for generation of new chemical spaces: Right now we are using natural product or general chemical libraries, but there is an opportunity for combinatorial libraries made of drug-like fragments. And we need tools in order to generate those. Essentially a tool to create new chemical space agnostic to the way you develop it, but the readout is simply whether you have a phenotypic change based on the activity of products that the particular component has managed to generate.
- Better target agnostic readouts on granular level: Because the readouts are so complex and we want to be target agnostic, the target should be for example a senescent cell and the readout should be whether the senescent cell is not senescent anymore after.
Talk – Joris Deelen
Biomarkers and how to move them to the clinic
- Benchmark and compare aging biomarkers with existing ones: We should try to use all the already identified aging biomarkers and test them along with biomarkers that are already used (cholesterol, triglycerides,..) and bring them to the clinic this way, and see if it can replace the things that are already used in the clinic.
- Harmonize clinical trials and blood biomarkers used in mice and humans: Not many biomarkers that are coming from the model organism are actually used in the clinic. What might help is testing more from the blood of mice and then harmonizing it with human blood (we usually do blood tests with humans, but with mice we take all kinds of tissues, which are not possible to collect from humans for wide use). And then testing an intervention that works in mice on humans and see exactly how it differs. Harmonize mice clinical trials to look more like human clinical trials, to mimic the human situation, which could hopefully make them a bit more translatable.
Utilize genetics of long lived people to move the field forward
- Go from long lived humans to animals instead of from animals to humans: A lot of research comes from animal models, which works well, however we sometimes do not see the same effect in humans. And in long lived humans, it’s really hard to find common genetic mechanisms between all these people that would explain why they managed to get so old. There might be another way to use the data from long live humans though. And that is developing interventions to those possible identified targets that we’re not sure about, and then test them in mice. So instead going from animals to humans, go from long lived humans to animals.
- Dampen rather than completely block: For example rapamycin completely blocks mTOR1, but we don’t see mTOR1 being completely blocked in long lived humans. There’s more of a dampening rather than shutting off completely, so we should look for interventions that are mimicking that.
- Create a study and a database with all different biomarkers and compare them: One actionable thing we could do would be to collect all different types of biomarkers and test them in a study. They have never been tested in the same study, and in the right study. Having such a database to make a decision about specific biomarkers and omics would be helpful.
- Develop markers and tooling enabling cheaper testing on a massive scale: Some of these markers are very costly though, so another action point is developing markers that are much cheaper. So we can get much more data and test the markers on larger scales. So doctors and researchers can use them and agree on them, because they are proven to be valid. If there isn’t any database like that yet, we should come up with one.
We’ve been looking at biomarkers in a very limiting way:
- There is a new biomarker used to diagnose Parkinson’s based on lipids in sebum. Incredibly cheap and easy way of doing it, based on one nurse’s ability to smell Parkinson’s patients. Similar to that, we as a field could be a bit more adventurous about the types of things that we consider as a biomarker.
Collecting samples from humans is impractical unless it’s blood, what are some other sample modalities?
- Maybe non or minimally invasive things like skin biopsy, urine, saliva. Problem is that it’s hard to get high throughput data for the less used modalities. That might be an opening for new tools that are able to do that. To bring something into the clinic, it needs to be high-throughput and easily measurable, and also standardized so we can compare between different studies. So we can do 1000s of samples for large studies and then clinical use. That is a challenge that we should be working on, so we can get bigger databases. There’s a focus on discovering biomarkers, but not much focus on making those measurements affordable and available in high-throughput fashion.
What should government funded research be doing with future centenarians to track them now? What does Nir Barzilai wish he would have done before starting the original study on centenarians with current tools?
- Nir Barzilai, Thomas Perls, AFAR, Regeneron are together starting a project with the aim to recruit the next 10000 supercentenarians, and get all the data (whole genome sequencing, electronic medical record,…) from them. They are also recruiting their offspring, because when looking at supercentenarians, you are never sure whether you are looking at what enabled them to live that long, or what will kill them in a year, because they’re at the end of their life. But offsprings should be a great treasure trove of the right data (for example IGF and HDL levels, which proteins they have even in youth and are inherited, or which special proteins they have, etc.).
Do methylome clocks on the offspring of supercentenarians look better than the age matched controls?
- They are just studying that, and it seems that they actually fall between the supercentenarians and controls. But supercentenarians are older, so the question is which methylation patterns are inherited – it is possible that the methylation patterns are inherited, so there’s a lot of work to do on the biology of methylation and not only the clocks.
Do supercentenarians have a shared mechanism or are there multiple mechanisms how to get there?
- We don’t know yet, but we might be able to see soon with the increasing amount of data that we run through models. The more data we have the more needles in the haystack. However even now, if we put the genetic differences among supercentenarians into pathways, they are really telling – insulin IGF pathway, mTOR pathway, AMPK pathway, sirtuin pathway. So it’s probably all there, we just need to know what to ask. Pathway analysis is a key to this, we cannot look at single proteins as biomarkers, because there are not statistically significant differences most of the time. But when we look at it on the systemic level, and take 50 proteins that are involved in the exact same biochemical pathway and they all shift in the same direction, then the pathway becomes really significant as a whole.