{"id":845,"date":"2019-07-01T16:15:24","date_gmt":"2019-07-01T16:15:24","guid":{"rendered":"http:\/\/joshmitteldorf.peachpuff-wolverine-566518.hostingersite.com\/?p=845"},"modified":"2019-07-03T09:34:53","modified_gmt":"2019-07-03T09:34:53","slug":"money-in-aging-research-part-i","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/joshmitteldorf\/2019\/07\/01\/money-in-aging-research-part-i\/","title":{"rendered":"Money in Aging Research, Part I"},"content":{"rendered":"

Part I : The Business Culture of Science<\/b><\/h1>\n

Since 2000, there has been a 20-fold increase* in research funding for anti-aging medicine.\u00a0 Wow! That\u2019s a good thing. But let\u2019s keep our eyes on the ball. There is danger that this welcome infusion of capital may be biasing research priorities toward those that are most likely to be profitable, and maybe even diverting the best researchers from the radical thinking that will change our understanding of biology.<\/span><\/i><\/p>\n

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Whoever discovers an effective age-reversal treatment is destined to become a multi-billionaire!<\/span><\/i><\/p><\/blockquote>\n

At first blush, this statement seems obvious, but that doesn\u2019t mean it\u2019s true.\u00a0 There are many historical examples of people who gave enormous gifts to the world, but struggled in their lifetimes for recognition and even for a livelihood.\u00a0 Schubert, Poe, and van Gogh are artists who died poor, while people after them reaped billions from their work. <\/span>Inventors who never profited from their inventions<\/span><\/a> include Johannes Gutenberg and Nikola Tesla, Jagadish Chandra Bose, and Antonio Meucci (<\/span>who?<\/span><\/a>).\u00a0 Reginald Fessenden invented radio a generation before Marconi.\u00a0 Rosalind Franklin got no credit for being the person whose diffraction data and analysis was stolen by Watson and Crick for their Nobel research on the double helix.\u00a0\u00a0<\/span><\/p>\n

More to the point, there have been great discoveries that had no commercial value, or even negative commercial value.\u00a0 Linus Pauling spent the last years of his life documenting the anti-cancer action of intravenous vitamin C. To this day, vitamin C is under-utilized and under-studied precisely because it is so cheap that no one can get rich from it.\u00a0 I believe that aspirin and metformin may be two of the most potent life extention drugs that we currently know about, but we can\u2019t be sure, because they are both long out of patent, and no private company can justify the investment to study them.<\/p>\n

Rumors abound about cancer cures and energy technologies that are being suppressed because they would derail two of the most profitable businesses in the history of capitalism.\u00a0 I don\u2019t dismiss such claims out of hand.<\/p>\n

If there were a drug that could increase average human lifespan by 15 years (with side-effects that were wholly salutary), there would be a dozen companies tinkering with it, adding a methyl group here or a double bond there, looking for a variant that might boost lifespan by 18 or 23 years.\u00a0 In fact, there is about a 15-year advantage for people who are in a loving relationship, have deep community ties<\/a>, assume responsibility for leadership, make lots of money<\/a>, enjoy frequent sex<\/a>, and remain close to young family members; in comparison, the typical middle-aged American is lonely, alienated, struggling financially, and sub-clinically depressed, with a life expectancy 15 years shorter than it could be.\u00a0 The most effective things you can do to increase your statistical life expectancy are psycho-social, but who is conducting research into optimizing the life-extending benefits of community and relationship?<\/p>\n

Diet<\/a>, exercise<\/a>, saunas<\/a>, and fasting<\/a> are life extension strategies that are promising and under-researched because there is no clear path to mega-profits.<\/p>\n

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What I believe<\/b><\/p>\n

I am convinced that the primary basis of aging is an epigenetic program.\u00a0 Systems that repair and protect our cells and tissues are gradually shut down<\/a>, and destructive systems including inflammation and apoptosis are ramped up at late ages. Gene expression changes, modified systemically by transcription factors that circulate in the blood.\u00a0 I believe that these blood factors are the holy grail of aging research. Control over aging will come when we learn enough about the basic language of epigenetics to reprogram gene expression with our interventions.<\/p>\n

The difficulty is that there are dozens of known epigenetic mechanisms<\/a>, of which only a few have been studied in detail.\u00a0 A few years ago, it was understood that modifying non-coding regions of DNA could affect the transcription of nearby genes (cis epigenetic signals), but now we know that transcription of genes far away from the modification can also be affected (trans signals<\/a>).<\/p>\n

There is yet more complexity: most hormones and regulatory molecules have secondary roles that affect transcription.\u00a0 Imagine an ecosystem of signal molecules that maintains itself homeostatically, but also changes with age. Sixty years ago, we learned that the genetic code is as simple as it can logically be; every codon three base pairs on a DNA strand is uniquely transcribed to one amino acid, and a protein is built by chaining these together in order.\u00a0 Today we are learning that epigenetics is about as complex as it can be. So in my paradigm, basic research in epigenetics is an essential foundation for anti-aging medicine. If we are lucky, a dozen synergizing interventions will do enough reprogramming to re-set the aging clock. Perhaps there is even a region of the brain<\/a> that is a common source for the molecules that induce age-related change.\u00a0 If we are unlucky, it may require re-balancing blood levels of hundreds of different substances.<\/p>\n

I am optimistic that this can be done, but it will require collaboration on a broad scale.\u00a0 The process is unlikely to end with a single patent-holder who can rake in $ billions. The secrecy and the balkanization of corporate research is slowing progress.<\/p>\n

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Biases in Corporate Aging Research<\/b><\/p>\n

For the last five years, Google CALICO<\/a> has been the 800-pound gorilla in the room.\u00a0 Of course, we welcome their funding, the legitimacy they lend, and their collective brainpower to our field.\u00a0 But they don\u2019t play by academic rules. They are not following the open-source \/ free-to-the-public model that has been so successful for Google in software.\u00a0 They trend secretive and are not collaborating with university experts outside their walls.<\/p>\n

CALICO isn\u2019t announcing its philosophy or paradigm, but we might guess from its lineage that their methodology is rooted in data mining and artificial intelligence.\u00a0 Other companies that have announced publicly that they are taking this approach include Unity Biotech<\/a>, InSilico Medicine<\/a> and Spring Discovery<\/a>.\u00a0 They have in common a data-intensive approach founded in theoretical agnosticism.<\/p>\n

Machine learning<\/a> has been used successfully to create algorithms that translate languages, that drive cars, and that recognize faces.\u00a0 The best thing you can say about this approach to anti-aging medicine is that it is free of the theoretical biases that have plagued aging research through the decades.\u00a0 The worst thing you can say about it is that it misses a fundamental difference between organisms and machines.<\/p>\n

Machines are designed by human logical minds, and each part is engineered to perform a single function and do it optimally.\u00a0 Organisms are evolved by a process that depends on results only and involves no logical thought. We have found empirically that in biology, parts tend to serve multiple purposes.\u00a0 Causes and effects are entwined in tangled feedback loops. Hormones and other proteins are likely to serve multiple, overlapping functions, some of which are metabolic and some of which are regulatory.<\/p>\n

With a homeostatic physical system, you can tweak it to the right and it will bounce back to the left some fraction of the distance, so that the net effect is to move to the right but with less than your original amplitude.\u00a0 With a homeostatic biological system, you can tweak it to the right and it may bounce back and end up further to the left. The canonical example of this is hormesis<\/a>, which is so counter-intuitive that it took experimental scientists two decades to establish its legitimacy among biological theorists.<\/p>\n

The Challenge of Using AI to Modify Aging<\/b><\/p>\n

Machine learning algorithms work by finding optimal paths toward a well-defined goal.\u00a0 The machine learning paradigm needs a clearly-defined goal as a prerequisite. In the previous triumphs of machine learning listed above, the goal was well-defined before the process was begun.<\/p>\n

Application of machine learning to anti-aging will require a quantified measure of biological age.\u00a0 This is what has held up the field in the past. We can measure lifespans in worms in a few weeks, but to measure lifespans in humans takes decades.\u00a0 Aging research needs feedback that is faster than this.<\/p>\n

Just in the last year, there are epigenetic clocks<\/a> based on methylation that predict future mortality and morbidity far better than any other metabolic test.\u00a0 The bottleneck now is the availability of methylation data that is correlated to anti-aging interventions. That is why I have promoted the DataBETA project<\/a> to collect methylation data from a diverse set of early-adopters of anti-aging interventions.<\/p>\n

Using theory-free computer algorithms to search for anti-aging interventions is better than going about it with the wrong theory, but it\u2019s not as effective as starting with the right theory.<\/p>\n

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This is larger than aging medicine<\/b><\/p>\n

The culture of business has had a profound impact on science in general, not just aging science.\u00a0 A hundred years ago, people who pursued science were motivated by pure curiosity and intellectual ambition, because there was little reward to be had.\u00a0 Today, science is a career for something approching 10 million people worldwide<\/a>.\u00a0 Then, science was pursued by dogged individuals.\u00a0 Now, science is managed by bureaucracies.<\/p>\n

More patents have been issued since 2000 than all of history before. It\u2019s often said<\/a> that the number of working scientists is 10 times greater than all the scientists who have ever performed research in the past, but the actual figure is more than 100 times.<\/p>\n

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Credit: Future of Life https:\/\/futureoflife.org\/2015\/11\/05\/90-of-all-the-scientists-that-ever-lived-are-alive-today\/<\/figcaption><\/figure>\n

The advance in scientific data reflects this increase, and more.\u00a0 To the extent that scientific productivity can be quantified, the productivity per scientist has increased as the number of scientists has advanced exponentially.<\/p>\n

What we don\u2019t have is exponentially more understanding.\u00a0 It\u2019s enlightening to compare the first half of the Twentieth Century with the second.\u00a0 The first half** brought us revolutions in understanding:<\/p>\n