Sramana Mitra: Let’s switch gears to another category. Pick another category where there is a heavy computer science element.
Sergey Jakimov: Another interesting one is Digital Health. If we’re talking longevity, it’s of course aging clocks and all sorts of consumer-facing risk calculators. That’s another very interesting angle of how we taught algorithms to work with clinical data or user-reported data as well as how we made it consumer-facing and consumer-digestible.
A very good example would be Deep Longevity. Deep Longevity developed agent clocks as a SaaS but they also have a consumer-facing part of it. This is where users can track their biological age versus their chronological age by feeding in data. Those data are phenotypic, or user-reported, as well as lab data. You can feed in your lab tests in order to understand how certain biomarkers correlate with your biological age and how it matches with your chronological age. What users find is their biological age is much more. That’s one example.
The other example is risk calculators. This is where we already have a lot of models that have now been digitized where users can assess their disease risks based on very simple user-reported metrics. A very good example is Breast Cancer Risk calculators that have been implemented on the state level.
New Zealand had a whole campaign of introducing this digital tool. We also have very good examples in the Baltics where companies launch these public-facing digital health applications where women were allowed to assess their breast cancer risk using a model. Then they offer actionable steps of how to prevent these risks.
The third angle is the digital health component which has to do with patient engagement. This is where there’s a lot of competition, but it is an untapped field in terms of working mechanics of how to engage patients for real-world data generation for their retention and clinical trials. You can write a book already on patient engagement solutions because a lot of companies have tried a lot of things but, realistically, the goal still remains the same.
How do you put the patient in the center of any clinical research or any sort of data-generation activity that the person is participating in? How do you ensure that consent is properly recorded? How do you ensure that the patient still remains motivated to stay in the trial or study? You can talk a lot about these things.
Sramana Mitra: You haven’t said much about the DNA side of it. Where does that sit right now in your investment thesis?
Sergey Jakimov: It depends on the angle. Consumer DNA testing can mean a lot of things these days. The reason why it can mean a lot of things is that DNA sequencing has defied Moore’s Law. It is becoming cheaper much faster than Moore’s Law. As a result, you have tons of consumer DNA-sequencing companies offering different degrees of detail and different amount of snips and different degrees of depth.
In our investment thesis, we haven’t invested in a single one of them. We have encountered a couple which dealt with health risks and identifying certain genes that would increase the capacity of the individual to develop certain age-related diseases. I’m talking about more specific ones that are less studied. We haven’t invested in those because we don’t feel like there’s a lot of longevity angle there.
That said, one of the biggest achievements of the 21st century when it comes to longevity and digital health is that the societal adoption of longevity ideas comes through self-cautiousness. That is delivered through the abundance of data we have around us. You can sequence your genome for $200. You have your Apple Watch that constantly monitors your vitals. You live in an environment that always provides you with actionable insights. That helps.
When asked about longevity, in general, we always say there are multiple levels. There is the first level which is awareness. That is where DNA sequencing companies belong.
