Having an overarching model (or several competing models) of which different parts can be tested independently seems like a structure which is very amenable towards different scientists, so I am disappointed none of the biological/medical community has started doing something like this.
This is actually done, quite a lot in fact, it's just really hard and the search space is huge. Kudos to you for your analysis; it's unlikely to be a major step forward, but given that idea search space is effectively exponential, it's also entirely possible that it's a unique insight. Please do attempt to publish it.
For an 'off the top of my head' example of this sort of modeling in the wild, this is a really interesting paper:
https://europepmc.org/article/pmc/pmc6520007
Basically, the model was "we've got a class of cancer cells with mitochondrial weirdness, what happens if we shut off both mitochondrial ribosomes at the same time?" And it turned out there were commonly available low dose drugs which do this.
Thanks.
Questions:
Why? 'EET-A will show temporary benefits as an anti-ageing therapy (70% as above) and will work "better" than* senolytics in that it will actually reverse ageing rather than needing to be taken at higher concentrations over time (40%).'
How would you obtain and how would you dose if you were performing a human study?
(and why 3 separate parts?)
Perhaps I am being too confident in it. I didn't have time to cite sources but the biology of AD seems to be a microcosm of the biology of ageing overall, and EET-A has shown a bunch of random unconnected benefits in mouse models (regenerating blood vessels after a heart attack etc.).
I do not know how I would obtain it (one would probably need free access to a chemical lab to synthesize it, just looking at EET and other analogues they seem relatively synthesize-able) as for dosing I would dose at comparable ppm levels to the rodent models.
I did 3 separate parts partially because I thought they seemed rather unconnected, and mostly because I was concerned about posting a very long and cumbersome post. Now that I look at it, it didn't really need to be three parts at all, it just felt a lot longer when I was writing it.
Epistemic status: Big if true, I don't have much time now but I might try and write part of this up into a more formal scientific letter to a journal or something later. I am reasonably confident in my models here but I do not have much experience in the field and I've written this up over the past weekend instead of revising for my exams.
Predictions
Results from EET-A human trials (if they go ahead) will improve patient outcomes in AD (60%), conditioning on EET-A being an effective agonist of PGC-1α in humans (80%)
EET-A will show positive results in models of HD (40%) low as I think the mechanism of mHTT toxicity (binding to DNA to prevent PGC-1α production) means EET-A cannot act upstream of it and can only act on the same level.
EET-A will show temporary benefits as an anti-ageing therapy (70% as above) (more like 35%, this has clearly slipped past proofreading and various sanity checks on my part) and will work "better" that senolytics in that it will actually reverse ageing rather than needing to be taken at higher concentrations over time (40%) (20%? These both seem wildly overconfident with some afterthought).
Retrospective
I think this perspective on AD and HD is probably useful, and it's not one I've seen before. Scientists are awful at sharing high-level models of diseases as they do not generally involve novel research and are (I suspect) very difficult to get published in high-impact journals. The authors of the paper using EET-A to treat AD did not seem to know why their treatment works, whereas I built a model whereby EET-A should effectively treat AD before I even considered "testing" my theory by simply looking to see if anyone else had done the experiment (Having made the prediction first gives me extra confidence in my own models but I don't expect it to be a strong argument to anyone reading this).
The remarkable thing about this investigation was how little time it took me, about three days. I expect there are many more intelligent and experienced people than me who could be drawing up similar links and conclusions with a small amount of effort. Building models of diseases based on existing studies is a clear way to guide new research, if I was a researcher about to start a new clinical trial (which could take years of my life) then even a 1% increase in success rate ought to be more than worth a short amount of analysis. I suspect this is not a mental habit which many scientists are in, perhaps due to social pressure to "stick to their lane" and just work on one small area of research. On the other hand maybe there are many scientists who are doing this but just not telling anyone.
If we are to cure ageing at some point (something I plan to be involved in) I suspect it will involve similar levels of modelling cellular processes. Having an overarching model (or several competing models) of which different parts can be tested independently seems like a structure which is very amenable towards different scientists, so I am disappointed none of the biological/medical community has started doing something like this.