A Sizable Fraction of Longevity Industry Companies Build Drug Discovery Platforms
Venture capitalists are characterized as exhibiting sheep-like behavior, though it would probably be more correct to say that the limited partners who invest in venture funds have this issue, and the venture capitalists must go along with it if they want to build a fund at all. Investment organizations are risk-averse in interesting ways, and near always prefer to put funds into a near clone of an existing effort that has shown traction rather than something novel. With this in mind, I'll note that a sizable fraction of companies in the longevity industry are drug discovery platform developers whose founders happen to favor mechanisms of aging as a target. The companies typically launch with only a declared agenda and the start of a platform intended to make small molecule drug discovery more efficient in some way; usually this involves machine learning.
To what degree are these companies multiplying because it is comparatively easy to raise funds with this pitch, versus this being a period of time in which advances in machine learning are genuinely offering many ways in which to meaningfully improve small molecule drug discovery? I'm not familiar enough with that part of the field to comment. Either way, work on aging seems like it might be something of an afterthought in the underlying mechanisms that have produced a prevalence of these initiatives. The iconic example of a computational drug discovery platform company in the longevity industry is Insilico Medicine, now largely pivoted away from aging in favor of selling capabilities in drug discovery to industry giants. On the other hand, BioAge appears to be staying the course to put some of their drugs into clinical trials. Gero could yet go either way. And so forth.
An any case, that said, Arda Therapeutics is another new drug discovery platform company that launched with big name seed stage investors, and a good philosophy of development related to selective destruction of problem cells in the aging body. There are many populations of cells that are small in number but cause outsized issues, particularly in the immune system, such as age-associated B cells, chronically activated microglia, and so forth. There are likely more such cell types yet to be discovered. On the whole, not much has been done to advance the practical removal of these cells to the clinic, outside the cancer and senolytics research and development communities. This is an area in which more initiatives are needed.
Arda Therapeutics: Targeting Cells to Treat Disease
Today, I'm excited to introduce Arda Therapeutics. Arda is taking aim at chronic diseases and aging by eliminating the pathological cells that drive these conditions. Our approach starts by using single-cell data to identify pathological cells and specific markers to target them. We then design therapies to eliminate these - and only these - cells. We are initially focused on treating chronic diseases, with the long-term goal of extending healthy lifespan.
The idea of eliminating - or "targeting" - bad cells is not new; most cancer treatments are based on this strategy. Yet when it comes to other diseases, rather than removing harmful cells, most therapeutics modulate the activity of individual proteins with the goal of modifying cell behavior. However, cell behavior is a consequence of complex regulatory networks: multiple pathways contribute, often with redundancy, making cell behavior difficult to change via single targets. We believe that in many cases the better strategy is to eliminate the entire pathological network - that is, the entire cell.
Our team combines expertise in pathological cell clearance with a rare blend of computational and drug development know-how. Still, there is no guarantee of success. Ten years from now, I believe there will be dozens of cell targeting therapeutics for chronic diseases. I hope many of them are Arda's. But if we fail at making successful drugs, we will at least succeed in mapping part of this new territory, making it a bit easier for others to take the next step. Ultimately, we are running the same relay race, and the trophy is more quality time for all of us.
...The companies typically launch with only a declared agenda and the start of a platform intended to make small molecule drug discovery more efficient in some way; usually this involves machine learning...
Thank god, at least we don't see stuffing blockchain everywhere anymore.
In software development the most successful frameworks/platforms are not designed but "harvested" from an existing implementation. The designed ones usually are solution in search for a problem . So now , when i here about a new discovery platform I know it is some VC/investor buzzword compliance.
While machine learning has an immense potential using it as the competitive advantage in discovery is ironic. A good discovery needs an idea. But in biology, what it needs the most is good experiments. And many of them. ML can be a good tool for metastudies. However, the current breeds like deep learning require huge to immense datasets to train it. If the datasets are small you cannot really train them or risk over-fitting.
The sad truth is NONE of these companies will end up doing anything for aging
They use it purely as window dressing for dumb money investors - In-Silico does this constantly
What typically happens in these shops is the "drug discovery as a service" model initially brings in some cash upfront, and some pharma partner name recognition, but it's never enough for adequate venture returns, and the investors end up having the company pivot towards a couple internal drug development candidates (low hanging fruit sorts) while all else gets lost to time
There should be no connection of this model to the longevity space - it's apples and oranges
Before criticizing the drug discovery approach when it comes to longevity in the context of Insilico, check out the publication list (https://scholar.google.com/citations?hl=en&user=8Icccp0AAAAJ), and the target discovery platform (www.pharma.ai). Without a better understanding of the biology of aging and disease, it will be impossible to make substantial progress. Our approach allows performing industry-scale research in biology while maximizing shareholder value (companies need to deliver returns to investors).
To learn more about the different credible business models from top VCs and learn from top scientists in the field, mark your calendars for http://www.AgingPharma.org - 9th Annual ARDD conference.
And before criticizing the investors supporting this industry, try to build a better business model and pitch to Y-Combinator and similar platforms. This should help.
If you are interested in hot jobs in longevity - attend the longevity job fair tomorrow:
https://lu.ma/odlb-job-fair
That's all well and good Dr. Zhavoronkov
But you know if/when your first drug hits a decent endpoint in the clinic, whatever the indication is, your investors will jettison the rest of your assets
No drug discovery platform is ever valued anywhere near a lead drug candidate
The history of the industry is littered with that graveyard
@Alex Zhavoronkov: Agreed that we need an escape from the regulatory capture business model hell that the medical industry is in; some other option beyond useless supplements/cosmetics and the crushing FDA path for therapies.