An Interview with Alex Zhavoronkov

The Life Extension Advocacy Foundation volunteers here interview Alex Zhavoronkov of Insilico Medicine. This company is focused on analysis of aging and discovery of drugs that might modestly slow aging rather than interventions after the SENS rejuvenation research model. If continuing along much the same road in the future, I predict that that the most important contribution to the field arising from this work will likely be a range of novel biomarkers to help determine the effectiveness of therapies that aim to treat aging. I have never been all that enthused by efforts to produce or repurpose drugs that tinker with the operation of metabolism to slightly slow aging, such as calorie restriction mimetics and the like. The plausible outcomes resulting from such efforts look marginal at best, and these research projects are at least as expensive as initiatives that aim at actual rejuvenation, while that rejuvenation has a far greater predicted outcome on health and longevity. On this topic, Zhavoronkov and I clearly differ in our expectations.

Your work focuses on computational medicine, how would you explain this relatively new field of science to our readers?

Computational biomedicine is a very broad field of research, where computational methods and tools are applied for diagnosis, treatment and research. The field has been around since the invention of electronic analytical equipment, but in recent years it got a major boost due to the availability in Big Data, increases in computing power, breakthroughs in machine learning and convergence of the many fields of science and technology.

You are the CEO of Insilico Medicine. What are the main goals of the company for the next 5 years? Can we expect breakthroughs in personalised medicine?

Our long-term goal is to continuously improve human performance and prevent and cure the age-related diseases. In 5 years we want to build a comprehensive system to model and monitor the human health status and rapidly correct any deviations from the ideal healthy state with lifestyle or therapeutic interventions. Considering what we already have, I hope that we will be able to do it sooner than in 5 years. One reason why we can manage over 170 projects is because we use agile development practices and approach every project as a software development project. We treat aging as a salami, constantly "cutting" thin slices and I think we are halfway through.

In 5 years you can definitely expect breakthroughs in personalized medicine and we are not the only company working in the field, so there will be many breakthroughs on the many fronts. The main breakthroughs I can promise from Insilico are in the area of multi-modal biomarkers of aging, where we take as much data available for an individual from simple pictures and regular blood tests to very expensive molecular and imaging data and turn it into a model, which can be used to make a broad range of predictions, recommendations and treatments. We are entering the era of personalized drug discovery and regenerative medicine.

One of our major contributions to the field was the application of deep neural networks for predicting the age of the person. People are very different and have different diseases. But if you want to find just one feature, which is biologically relevant and can be predicted using many data types - it is the person's date of birth. So we build all kinds of predictors of chronological age and then look at what features and at what levels are most important and can be used to infer causality and be targeted with interventions. I think that this approach is novel and will result in many breakthroughs.

How do you decide what projects to get involved in?

The way we prioritize projects at Insilico Medicine is by looking at the number of quality-adjusted life years (QALY) each project can generate. Most pharmaceutical companies, governments, and philanthropists do not realize that aging research generates the maximum number of QALY per dollar spent. It is the most altruistic cause and the most effective investment. If you add just one year of life to everyone on the planet, you generate over 7 billion QALY. The average reasonable cost per QALY is around $50,000. So it is possible to generate several hundred trillion dollars by extending life of everyone on the planet with a simple intervention.

What is your estimate, when we could expect the first powerful treatment to slow down aging appear on the market?

I think that there are several very powerful treatments that are already available on the market and to get the extra 10-20 years or even more we just need to devise a way to turn these into therapeutic regimens. I think that a comprehensive regimen involving metformin, targeted rapalogs, senolytics, anti-inflamatory agents, aspirin, NAC, ACE inhibitors, beta-blockers, PDE5, PCSK9 inhibitors, NAD+ activators and precursors in combination with the regenerative medicine procedures and also a set of cosmetic and lifestyle interventions could easily add 20 years to our life span. And I am sure that some people are already trying these interventions on themselves. Unfortunately, nobody is tracking this data.



@reason that said it can also be used to refine drug searches making things faster. For example we could use it to look for potential AGE breakers or amyloid clearing candidates. Anything that can reduce the complexity of the task is a win win in my book. We are going to be using AI modelling in our future project as part of our process for example. Biomarkers are of course another likely result of this line of research. So I see insilico (the field not the company) as being useful to SENS and SENS like repair approaches.

Posted by: Steve Hill at April 19th, 2017 9:02 AM

@reason: I support SENS and Aubrey. Insilico Medicine found a way to make aging research sustainable and informative going forward to ensure that there is no need to spend most of the time looking for the funding. Plus, by focusing on AI we made major progress in the many disease areas and developed a set of tools for dimensionality reduction, reconstruction of incomplete data setsand one shot learning. But thank you for the criticism.

Posted by: Alex Zhavoronkov at April 21st, 2017 8:17 AM

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