What if Geroscience Fails to Deliver? Separating Hype from Reality: Pragmatic Optimism for the Longe

Kate Batz, Managing Partner at Longevity.Capital In my previous article, I discussed the issue of Longevity Hype in Silicon Valley, the potential risks it poses to the success of the Longevity Industry as a whole as well as the opportunities to be realized both in California and globally by proactively countering ‘hyped’ approaches thus paving the way for more validated and realistic ones.

In this piece, I would like to delve a bit deeper into the root cause of the issue behind the so-called “Silicon Valley Longevity Hype” and elaborate on my views on how associated challenges can be transformed into opportunities for the growth of the sector as a whole in a more balanced, harmonized and most importantly - realistic manner.

The Rise of Longevity Hype

Back in 2013, Silicon Valley tech giant Google promised the world that it will solve the problem of death. We have entered a new decade now, however, in my opinion, the progress in actual, practical life extension of humans is not far away from where it was back in 2013.

There is lots of positive hype on the subject: many people ranging from general public to scientists, entrepreneurs and investors are confident that we are on the brink of creating actual human life extension techniques which will soon translate into real-world, accessible applications. I aim to examine the evidence for whether or not such a claim is validated as well as offer a set of guidelines to help separate hype from reality around this hypothesis in a concrete, logical and tangible way.

Indeed, a number of Longevity-focused scientists have achieved a rather significant progress over the last decade with respect to stalling the aging process and in some cases even rejuvenation (restoring a young phenotype) in certain model organisms such as yeast, worms, flies and mice.

For instance, in January 2020 researchers at MDI Biological Laboratory, in collaboration with scientists from the Buck Institute for Research on Aging and Nanjing University in China, identified synergistic cellular pathways for Longevity that amplify lifespan fivefold in C. elegans, a nematode worm used as a model in aging research, which succeeded in extending their lifespan by 500%.

In another recent example (October 2019), Maria Blasco and colleagues managed to extend the lifespan of mice by 24% by breeding a set of chimeric mice using embryonic stem cells with telomeres twice as long as usual.

The number of headlines highlighting substantial increases in both the lifespan and healthspan of model organisms has grown dramatically in the past decade. However, while we see significant progress in model organisms, this is not the case with respect to humans. This brings back one of the most important points of my previous article - namely, the overwhelming confidence of Silicon Valley-based Longevity companies that positive results in model organisms will translate into comparable outcomes in humans. Such an assumption is something that Deep Knowledge Group’s Longevity-focused hybrid investment fund Longevity.Capital takes specific measures to avoid and de-risk by means of mandating preliminary positive results in humans as a core factor of its scientific due diligence for biomedical companies as well as by strategically prioritizing other sectors with high degrees of market-readiness that are not exclusively focused on biomedicine, such as AI for Longevity, AgeTech and Longevity FinTech.

While the issue of transferability of positive outcomes in model organisms to humans has long been a concern for some of the more forward-thinking specialists in the field, it is only recently that actual hard data on the matter has come to light. For instance, on February 14, 2020 Science published a study titled “Translating preclinical studies to humans”.

The article summarizes many of the issues at the heart of the difficulties in translating drug successes in model organisms into humans and provides some suggestions on how AI could be used to shift toward more human-centered approaches: “Systems biology and machine learning (ML) can be used to translate relationships across species. Instead of attempting to “humanize” animal experimental models, which is possible only to a limited extent, greater success may be obtained by humanizing computational models derived from animal experiments”. Suffice to say that Deep Knowledge Group has always been logically skeptical regarding over-valued results in model organisms and overconfidence in their ability to translate to humans, and welcome the latest science which is backing up and validating those concerns.

In short, in contrast to the growing volume of positive results in model organisms, advances (or lack thereof) in practical human life extension and understanding of human Longevity are at a comparative standstill. Our position is that with the right set of approaches and methodologies, science could have progressed twice as much in terms of human healthspan extension compared to actual results achieved during the past 10 years.

In our opinion, one of the factors behind this comparatively slower rate of progress is that aging research itself overvalues certain specific technologies and domains over others. With the enormous volume of resources behind Tech giants like Google and Amazon as well as many biotech, biomedicine and healthcare companies, we should have seen much higher success rates of practical, tangible developments aimed at extending human life and healthspan, enabling us to determine which research sectors are overfinanced and which are underfinanced - however, this happens to not be the case.

Below are the examples of capitalizations (as of February 26, 2020, source: Yahoo Finance) of public companies focusing on Longevity:

As we can see, unfortunately most of the publicly traded entities which can be viewed as Longevity-focused companies exhibited declining capitalization.

Undoubtedly, gaining a sufficient understanding of the nature of human aging and Longevity as well as the necessary scope of technologies required for practical human life extension to the point of achieving Longevity Escape Velocity (the point where more years are added onto the human lifespan than are taken away due to aging) would require substantial resources. However, compared to the amount of funds being spent even on general aging research, not to mention the myriad of diseases that have their root causes in aging, we are not talking about unthinkable numbers. We estimate that 100 billion USD per year over 10 years would be more than enough to get a real understanding and implementation of the technologies necessary for practical extension of Healthy Human Longevity, which is vastly less than the amounts currently being spent on cancer research or on FinTech, for example.

Furthermore, even if the aforementioned amount of funding was in fact dedicated to such a pursuit, we would still face the challenge of executing it in a sufficiently structured, harmonized and balanced way, thus creating a risk of delay in completion amounting to 5-10 years unless implemented in a maximally relevant and strategic manner in the first place.

The Longevity Industry and its scientific and R&D communities need to reach a consensus on what roadblocks need to be overcome in order to achieve a critical inflection point, Longevity Escape Velocity, before 2030 (which we believe is a reasonable timeline, provided the necessary funding will be available) rather than during 2035-2040 time period, as well as what bottlenecks could be optimized to accelerate the process even more (i.e., to a timeframe of approximately 7-8 years rather than 10).

In order to do this, we need to think about the challenge not as doctors or biotech scientists, but as aerospace engineers. In building a rocket, some parts (e.g. the navigation system, operating system, engine, etc.) require more R&D resources than others in order to meet the target deadline. Figuratively speaking, if we want to launch the Longevity rocket by 2030, more time and resources will need to be allocated to certain parts. And if we wanted to achieve a pilot launch date 2-3 years sooner, what are the precise components and fundamental bottlenecks that need to be prioritized?

Today, similar to 10, if not 20 years ago, progress in human life extension research is comprised of several key pillars:

The First Pillar: Experiments in Model Organisms

The first pillar of human Longevity research is the well-established but outdated practice of experimentation on model organisms with the expectation that their outcomes will translate to humans. This has been the consensus paradigm followed in the traditional biomedicine and biotech industry for the past 50 years, and has consistently shown that in the majority of cases drugs do, indeed, fail to translate to humans with sufficiently high effectiveness or acceptably low adverse effects to make it to market.

Given the approximately 90% failure rate of clinical translation into humans in the comparatively simpler biomedicine, biopharma and biotech industries, which deal with interventions with far fewer complex components (e.g. single molecules) targeting far simpler biological systems (e.g. individual disease targets), it is reasonable to expect higher failure rates for multidimensional therapies with many “moving parts” that target significantly more complex biological processes.

The source of this high anticipated failure rate is twofold. The first fundamental factor is the vast biological (physiological, genomic, epigenomic, etc.) differences between humans and model organisms, both general and in terms of the actual nature of aging in particular, which is scientific. The second is not so much a scientific (and, therefore, fundamental) problem, but one embedded in honest but flawed human nature and error - the fact that by current estimates as many as 70% of scientific experiments, studies and articles are not repeatable, due to either well-intentioned mistakes or intentional fraud. This phenomenon is often referred to as science’s modern “replication crisis” and will pose even more challenges for the expected success rate of human-focused Longevity therapeutics.

The Second Pillar: Biohacking and Quantified Self

The second pillar is diametrically opposed to the first - the biohacking and Quantified Self movements, where individuals attempt to push the limits of the preventive medicine methods and technologies available to them to the extreme, utilizing the full arsenal of tools at their disposal (including those outside of the scope of FDA-approved treatments) in order to optimize their state of physical, biological and cognitive health and performance.

California is home to a community of scientists practicing self-experimentation and so-called Quantified Self methods by using advanced approaches and techniques related to precise diagnostic and monitoring as well as implementing safe applications of geroprotectors. Such approach can be viewed as a more balanced and pragmatic alternative to biohacking as the latter is perceived as a more radical option.

The Third Pillar: The Treatment and Neutralization of Age-Related Diseases

The third pillar has been around for much longer than the first two and consists of the treatment of individual age-related diseases such as cancer, diabetes, dementia, cardiovascular and cardiometabolic disease.

On its face, this pillar appears to exhibit the least fundamental potential in terms of practical life extension as it targets the end products of aging, i.e. traditional diseases, without in any way modifying their root cause. However, this pillar so far has achieved the greatest level of tangible real-world results in terms of human life expectancy.

For example, due to recent progress in cancer therapy, breakthroughs that were still in the R&D phase 3-7 years ago (e.g., cancer immunotherapies) are being used in practice today, which has resulted in the largest ever single-year drop in the US cancer mortality rate in 2016-2017, equal to 2.2%.

Additionally, whereas during most of the history researchers and companies were focusing on treating age-related diseases and minimizing their detrimental effects, in more recent years we have seen an increasingly prioritized emphasis on preventing or delaying their occurrence.

The reason that this pillar is the one with the highest rate of progress and real-world results, despite being the one with the least fundamental potential to increase either lifespan or healthspan, is due to two key factors. First, age-related diseases receive an enormous amount of funding and resources allocated by government and industry for their treatment. Second, the morbidity associated with age-related diseases offers clear and measurable targets for therapeutic interventions.

The Fourth Pillar: AI, Data Science and Mathematical Technologies for Longevity

The fourth pillar, which Deep Knowledge Group views as the one with the greatest potential to create real-world impact on human Longevity in a short timeframe, and the one with the highest cost-effectiveness ratio, is the application of AI and data science to Longevity. Unfortunately, despite being the pillar with the greatest promise, it happens to be the most underrepresented and underfinanced one within the global Longevity Industry.

The are many reasons for the enormous potential of this pillar:

First, Longevity is unprecedentedly complex, both as a science (dealing with the deepest levels of biology, health and disease) and as an industry (being composed of the intersection of many distinct, individually complex domains of frontier science and technology). AI, data science and mathematics are being applied in the R&D precisely for the purpose of processing data that is too voluminous and complex for humans to address manually - it is the engine not only for neutralizing complexity but also for yielding its power to create new-positive results.

Second, with the inevitable increase in distinct data points on the nature of aging, the number of specific biomarkers of aging and Longevity as well as amount of distinct Longevity therapies and technologies, AI will become the only tool for managing this enormous volume of data, both as it applies to P4 Medicine and Precision Health (real-world practical implementation of Longevity technologies) as well as with regard to the core Longevity R&D (which will likely not reach the level of marked-readiness for a number of years).

Third, AI is an industry vertical that is very well funded, with leading nations currently competing to win the global AI race to develop and secure the most advanced AI technologies and IP and to capture the highest densities of AI specialists. Ongoing developments in core AI innovation in and of itself are rapidly implementable (being a virtual, digital technology that can be replicated, transmitted near-instantaneously, and utilized at zero material cost once developed), thus being capable of having immediate accelerative impact on Longevity.

Fourth, AI is a self-evolving and self-accelerating technology in the sense that the latest advances in the field make it easier to develop the next paradigm shift in AI, consequently invoking an exponential effect.

Fifth, many technologies and techniques for extending Healthy Human Longevity, for preventive medicine and for maintaining an optimal state of precision health are already innovated, validated and ready for use, however, they lack an infrastructure for scaling them to the masses. This is why we predict that the vast majority of practical, real-world effects in terms of extending healthspans in the next several years will come from existing, validated technologies, thus making it a data aggregation and analysis challenge, rather than a biomedical or biotech R&D problem.

In our opinion, AI for Longevity is the “smart money” sector of the industry which can achieve tremendous results and accelerated timelines in terms of progress in actual, tangible, real-world Healthy Human Longevity, even with modest levels of funding compared to other sectors.

Deep Knowledge Group predicts that this is the precise role AI will play in the Longevity space during 2020 - 2025, i.e., the aggregation, development and deployment of biomarkers of aging, health and Longevity, Preventive Medicine diagnostics and prognostics, Precision Health technologies and therapeutics as well as integrated wealthspan-extending AgeTech and WealthTech solutions for financial wellness across extended periods of Healthy Longevity.

The apex of this use case and its most robust and advanced embodiment consists of a digital avatar of the full human body monitoring thousands if not tens of thousands of personalized biomarkers (with at least several hundreds of precise biomarkers of aging and Longevity), both biological as well as psychological and behavioural.

CONCLUSIONS

• Once the logic makes itself apparent to all stakeholders, Deep Knowledge Group predicts that AI for Longevity, given its potential to out-compete all three of the other pillars, will shift from being one of the least-prominent to one of the most prioritized sectors of the global Longevity Industry, achieving groundbreaking results in record timeframes, even if given just a small fraction of the total amount of Longevity Industry funding;

• The first pillar, which absorbs the greatest levels of attention and funding, will continue to advance because it is a proven success within its own domain (positive results in model organisms), and will inevitably develop further. Biohackers and Quantified Self practitioners will be able to utilize techniques and technologies for optimization of health and performance which are reaching the level of practical implementation / market readiness and are continually improving. The progress in treating age-related diseases will continue almost automatically as the entire healthcare and biotech industry are already focusing on this pillar;

• Thus, even if the first pillar fails due to the major roadblock of transferability to humans, it is likely that we will see tangible, real-world progress in human Longevity given the progress of the second, third and fourth pillars, especially in the near-term, where progress in Longevity will come from existing, validated market-ready technologies, and not moonshot biotech R&D;

• Most importantly, if enough resources are allocated and high priority is established with respect to the fourth pillar, it is entirely possible to achieve in approximately 7-8 years what otherwise could have only been accomplished in 10 years. What the fourth pillar needs for this to become a reality is intelligent coordination among experts and industry stakeholders (AI specialists, Longevity scientists and entrepreneurs, investors and governments, etc.) to achieve truly synergetic and self-accelerative results;

• While the above conclusions apply globally, they are particularly true with respect to regions like Silicon Valley that are home to a considerable level of hype and a prevalence of over-valued companies and technologies. The time it takes for us to achieve Longevity Escape Velocity is directly related to our efforts to proactively and pragmatically de-risk Longevity and neutralize Longevity hype by using validated approaches and providing sufficient funding and resources to sectors that show the greatest prospects for fast, efficient and economical real-world results, i.e. those backed by reality, not hype;

• Whether the progress within the first pillar will lead to successful transfer to humans is up to discussion as there is a dearth of evidence supporting this to-date. The second pillar is likely to bring about positive results sooner or later. The third pillar is already rendering successful outcomes - we believe that in 10 years Pharma will be able to treat or at least delay many age-related diseases and therefore prolong life and healthy Longevity by about a decade on average. Hence, even if the first and second pillars fail to deliver tangible results, the third pillar will be able to compensate for that. In our opinion, it is the fourth pillar that will not only deliver results not achieved by the first and second pillars but also accelerate the progress of the third pillar - thus, the fourth pillar will ensure progress regardless of the success or lack thereof of the other pillars.

Kate Batz Managing Partner, Longevity.Capital

If you are interested in the topic of Longevity, this subject will be explored in detail in the upcoming book by Longevity.Capital and Deep Knowledge Group Co-Founders Dmitry Kaminskiy and Margaretta Colangelo: "Longevity Industry 1.0: Defining the Biggest and Most Compelex Industry in Human History" - you can download the book's summary and/or pre-order the book at this link.

Deep Knowledge Group is a consortium of commercial and non-profit organizations active on many fronts in the realm of DeepTech and Frontier Technologies (AI, Longevity, FinTech, GovTech, InvestTech), ranging from scientific research to investment, entrepreneurship, media, analytics and more. Its subsidiaries and associated organisations include Deep Knowledge Ventures, Longevity.Capital, AI-Pharma.Capital, Longevity FinTech Company, Deep Knowledge Analytics, Aging Analytics Agency, Biogerontology Research Foundation, Longevity Swiss Foundation, Longevity International UK - Secretariat for the UK All-Party Parliamentary on Longevity, and AI-Longevity Consortium at King’s College London.

Longevity.Capital is a hybrid investment fund specifically focused on the Longevity Industry, backed by seasoned professionals who have been active in both the investment banking and Longevity industries for 25+ years, long before the sector was recognized as a serious prospect by the overwhelming majority of investors. The fund employs advanced InvestTech solutions for investment de-risking, including portfolio diversification across the full scope of the Longevity industry (biomedicine, finance, tech), and formulates its investment strategy based on sophisticated industry intelligence and comparative analytics provided by the world-leading Longevity Analytics entity Aging Analytics Agency, which uses hundreds of quantitative and fact-based parameters to identify prospective investment targets for the fund, utilizing multidimensional analytical frameworks as complex as the industry itself.