At the Ending Age-Related Diseases 2019 Conference in New York City, we had the opportunity to interview Dr. Justin Rebo from the drug discovery biotech company BioAge.
BioAge is developing a drug discovery platform that uses machine learning and artificial intelligence to discover targets that have the potential to promote healthy lifespan (healthspan) by slowing down aging and the ill health that it brings.
As the vice president of in-vivo biology at BioAge, Dr. Rebo leads the company’s internal in-vivo platform to find and assess the viability of new druggable targets for aging diseases and biomedical regeneration. With considerable business as well as academic experience in the aging field under his belt, Justin joined the BioAge team in 2018.
We had the chance to interview Dr. Rebo about his work at BioAge during the conference. During the interview, we had a chance to talk about a wide range of topics, from data-driven approaches to drug development, the necessity for good biomarkers, epigenetic clocks, and his experimental work at Berkeley in heterochronic blood exchange to the advice he would give budding entrepreneurs in the aging space and whether he sticks to a specific lifestyle regimen in order to maximize his health and lifespan.
A lot of people that are not scientists or are not in this field are still relatively unfamiliar with the use of data-driven approaches in aging research. I was wondering if you could briefly explain how BioAge verifies whether the aging targets they identify are valid?
Not a problem. BioAge begins with human data. We find human cohorts that have banked blood samples from decades ago, coupled with electronic health records that have followed those people ever since, in some cases, until their deaths. We send these blood samples for deep omics profiling: proteomics, metabolomics, transcriptomics, stuff like that. From that, we can find what’s in the blood, for instance, the transcription profiles of the blood cells and soluble protein metabolites, which is correlated with age-related diseases and mortality. That’s only part of the picture, of course, because that doesn’t tell you what’s causal, it only tells you what’s correlational. So, from there, we adopt a systems biology approach where we connect the results to whatever datasets we can find out in the world or among those we generate ourselves, which gives us a few extra clues. However, ultimately, the only way we can really verify if a target is valid is by testing it experimentally, and so that’s what we do. After we pull everything together data-wise, that only gives us so much. We really need to test these targets in animal models as well as cell models, but we prefer to test in vivo.
Therapeutic development for some age-related diseases (such as different tauopathies) is slowed by the lack of accurate biomarkers to identify them and their progression as well as by the lack of pharmacodynamic markers of target engagement. For metabolic diseases, on the other hand, several reliable biomarkers have already been identified, and work has started on developing interventions for them. Since research budgets are limited, what is your view on how these budgets should be allocated across these differing priorities, i.e. should the identification of biomarkers for more diseases or the development of interventions take precedence if we need to choose, and why?
That’s how BioAge started: the whole point was to generate biomarkers. At the same time, these so-called biomarkers are often themselves druggable targets. Part of the evolution of BioAge as a company is that first we find these biomarkers, and then we turn them into drugs. In terms of how society should allocate resources (biomarker research versus the development of interventions), we personally don’t have to consider that as much, seeing as we’re doing both in-house. I can’t really say what anyone else should do, but I think we found something that works for us.
Aubrey de Grey, in his keynote speech, was talking about clustering types of damage into categories to increase the pace of therapy development. His idea is that if we develop a therapy for one subtype of damage, it will be much easier to extend that therapy to similar types of damage. Since BioAge is working on using computational approaches to find the molecular pathways that drive aging, I was wondering if you are using a similar type of clustering approach to facilitate faster intervention development?
To some extent, because I love Aubrey’s integrated approach. For me, personally, his ‘seven deadly things’ talk was kind of my introduction into the field back in 2004/2005. But BioAge, at least initially, takes an opposite approach in the sense that we don’t cluster things. We look for mortality as our first differentiating factor. Any target that we look at as something that we might want to pursue must be associated with mortality, and mortality is really as broad as it gets. That being said, once we’ve screened for mortality, we then examine what specific disease indications would make the most sense. I can’t really get into detail about what those are.
But I like the way we look broadly at the data first, in a kind of “hypothesis-free” sense, with an open mind, letting the data speak for itself.
That’s what the whole idea of “data-driven” means, right?
Exactly! We have to have an open mind. The universe is the way it is. It doesn’t care what we want it to be, so our job is just to find out what that is.
What are your views on the usability and value of general and hallmark-specific epigenetic clocks?
The initial Horvath clock was actually quite good at doing one thing, and that was telling you what the age on your driver’s license or passport said. Which isn’t exactly what we need. We need a clock that will tell you how much longer you’re going to live or what diseases you might develop. There have been advances where they’ve actually trained the datasets on information that, instead of telling you how old you are, tells you what kind of diseases someone is likely to get and when they might get them. I think those show great promise. For us, epigenetic clocks are most useful as a tool to measure the efficacy of drugs we’re developing for various targets. I think all different kinds of (robust) biomarkers are useful tools in developing interventions.
You’ve done work on heterochronic blood exchange at Berkeley, finding that this approach acutely, rapidly, and effectively reverses the age of tissue to younger states (and was even capable of producing older states). Do you think we could and should bring extracorporeal blood manipulation for rejuvenation benefits to human application?
I developed a device that could, in a controlled manner, exchange blood between animals for whatever period they (researchers) want to: plug them in for like 20 minutes, do that every day or do it a single time. For the work that we published in Nature Communications, that was all done within a single day. It’s really a short-term exchange, but we still saw many of the phenotypic changes that you see with parabiosis.
I think that this is a valuable research tool, but I do not think that it is directly translatable to humans, for a variety of reasons. One, you can’t do parabiosis on people, but, scientifically, it also doesn’t make sense because when you’re talking about doing this between young and old animals, they are syngeneic. They’re almost clones of each other. There’s really no risk of any kind of immune reaction to the foreign blood or plasma. However, in human beings, we have a fair amount of experience with plasma exchange, for example.
This is typically done to replace someone’s plasma because this person has a highly acute autoimmune disease that is going to kill them if they don’t get rid of the antibodies that are in their plasma. This is a means of washing those out. What we also know from this work is that there is a severe immune reaction to the foreign plasma in a fairly reasonable percent of the cases.
This also increases with more infusions over time. A reaction is noted by the physician, so not necessarily a severe one, in as much as 50 percent of the time. I would not expect any beneficial effect you might get from transfusing young blood in an old individual to be enough to balance the effect of the immune reaction that it would likely cause.
I think the correct way to do something like that would be to essentially adopt BioAge’s approach: find the factors that are positive or negative, and then manipulate those directly. Sometimes, the drug itself would literally just be a recombinant protein that helps to increase the levels of the protein that is beneficial, and there are a number of ways to do that.
Are the aging targets you are identifying from biobanked blood the same across different biobanks and populations? In which class of targets do you see the most diversity?
That’s a really good question. The answer is sometimes yes and sometimes no, but enough of them are strongly in common across different populations and banks. I can’t really get into more detail about which class of targets we’ve found to be more or less diverse though.
You’ve also been involved in several startups in the biotech industry focusing on senescent cell removal, immunology, and regenerative medicine. Could you share some advice on how scientists looking to make the switch from academia to industry or others interested in starting a company in the rejuvenation field could best go about this?
I co-founded my first biotech company in 2010. Immediately after I finished medical school, I moved to the Bay Area. There are a few centers in the world with a concentration of capital: the Bay Area is one, Boston is another, New York is also one. I think you need to go where the money is, so you can immerse yourself in that environment and go to the right meetings, talk to the right people.
So you’d say it’s about talking to the right people when you have a good idea?
Well, I’d say it’s a few things, and you have to do them all right. You have to have a compelling idea that is really worth something. Without that, it doesn’t matter where you go. But you also have to meet the people that are interested in helping you bring that idea forward, and itis up to you to find them! Sometimes, these people will find you: there are cases like that, where the money comes to someone. But that’s definitely the exception. Even if you think that might happen, you’re still better off helping push that process along.
During the conference, Dr. Michael Lustgarten talked about his personal experiment with rejuvenation through nutrition, showing that an assay of blood markers shown to be important in aging, such as albumin, are at roughly 30 yo levels while he is currently 46 years old. Are you personally using any compounds such as metformin or fisetin, or are you applying nutritional regimens to yourself for rejuvenation benefits? If not, why?
I think it’s highly beneficial to follow healthy diets like the Mediterranean diet, for instance, and I think it’s mildly beneficial to exercise. But the fact of the matter is that these kinds of interventions are not going to have a large advantage on your mortality. We need real biotechnology to do that. I’m not really taking anything myself. I don’t think there’s enough evidence to support just about any supplement that is out there.
Look at it this way: there are a few things that you can do to improve your mortality quite a bit. Number one: stop smoking. Smoking doubles your risk of mortality at every age, meaning it takes about 10 years off of your life. Being mildly overweight has very little effect, but being very overweight does. Basically, I think that if someone has a good (rejuvenation technology) idea, they’re better off trying to develop that technology rather than trying to hack their way to a longer lifespan. I don’t think we have the tools all together to do that very effectively right now.
We would like to thank Dr. Rebo for taking the time to conduct this interview with us.
Butte, A.J., Ito, S. (2012). Translational Bioinformatics: Data-driven Drug Discovery and Development. Clinical Pharmacology & Therapeutics, 91(6), 949-952.
Faber, M.S., Whitehead, T.A. (2019). Data-driven engineering of protein therapeutics. Current opinion in Biotechnology, 60, 104-110.
Khanna, M.R., Kovalevich, J., Lee V.M., et al. (2016). Therapeutic strategies for the treatment of tauopathies: Hopes and challenges. Alzheimers Dement, 12, 1051-1065.
Rebo, J., Mehdipour, M., Gathwala, R., Causey, K., Liu, Y., Conboy, M.J., Conboy, I.M. (2016). A single heterochronic blood exchange reveals rapid inhibition of multiple tissues by old blood. Nature Communications, 7, 1-11.