61–70 of 72 posts
Earlier quoted context omitted.
Russians in particular?
I don't know if you've heard, but Russia and the USA haven't been best buddies for quite some time...
I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all). The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identit…
This paper is mainly a flag planted so they can claim they landed on mars first and fastest.
Earlier quoted context omitted.
I think this is explained pretty well in the paper: "...hepatocellular carcinoma (HCC) was nominated as the indication of interest due to its high prevalence in liver cancers and lack of effective treatments. In general, by analysis of text and OMICs data from 10 database for hepatocellular carcinoma, PandaOmics provides a top list of 20 targets after multiple dimensions filtration, including novelty, accessibility b…
I'm too dumb to understand that. For people like me, after some Googling, I got: hepatocellular carcinoma is a rare form of liver cancer (affecting less than 200k people in the US per year). CDK20 is strongly associated with that cancer / disease. This molecule inhibits CDK20 - so it might help people with hepatocellular carcinoma. But I'm dumb and know nothing, so that someone correct me if I'm completely wrong. I t…
They focused on a particular protein molecule called CDK20 which appears to be important for development of HCC. You can think of it this way... "If CDK20 goes 'haywire', it can contribute to the development of HCC."
The idea is that if you can stop CDK20 from going haywire, perhaps you can slow/stop/prevent/reverse development of HCC.
Along those lines, if you can find a "small molecule" that stops CDK20 from going haywire, that small molecule could potentially serve as a medicine for treating HCC. "Small molecule" is a common pharma term for molecule that is smaller than most biological "macromolecules" like proteins. "Small molecule" is often used (roughly) interchangeably for a molecule that can be developed into an ingestible pill (or injected). "Small molecule" medicines are often relatively stable (i.e., can be stored for a long time without many restrictive storage conditions), cheap to manufacture (not always), etc.
These people used computational methods to create several candidate small molecules that might stop CDK20 from going haywire (the 'technical term here is that the small molecule inhibits CDK20. Some of this was done in conjunction with AlphaFold's predicted 3-D structure for CDK20.
But just because the computer says that your small molecule might inhibit CDK20, that doesn't mean that your small molecule will _actually_ inhibit CDK20 in the real world.
The first _true_ test is to make the small molecule and experimentally assess whether it inhibits CDK20.
One of their candidate small molecule compounds appears to inhibit CDK20.
That's one of the punch lines of the paper.
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I don't like it when people are too negative about this stuff, but I'm going to be a little negative here.
Other than employing AlphaFold, this seems like pretty standard work for pharmaceutical development. I worked for a company doing structure-based drug design and the general concepts employed in this paper are not different from what wewe (and others) have been doing for a while.
They have a particular platform for finding these types of molecules and they would like to argue that their platform is unique and distinguishes itself from everybody else's platform.
I'm not saying that the candidate small molecule is "bad" or anything like that. It's definitely a promising lead and it needs to be pursued. But it's just a lead.
A lot of biotech / pharma involves hyping/selling your particular drug discovery platform and trying to convince people that your platform is the better/more efficient way of finding valuable drugs.
Just my 2 cents.
Earlier quoted context omitted.
I don't know if you've heard, but Russia and the USA haven't been best buddies for quite some time...
Have they ever?
I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all). The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identit…
I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all). The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identit…
First, they didn't discover a drug - they found a hit. 30 days from target to hit using conventional high-throughput biochemical screening would take 2-4 months. So, this is 3x faster, but that's not the rate limiting step. Validation and in vivo studies will take >4 mo and 1-12mo respectively.
Second, if we take this as a "we found a hit" paper, I want to know how specific your hit is. This would be one of the major advantages of using AF2 - screen against related proteins with some structural or functional similarity. This is the time intensive and oft overlooked part of good in vitro screening campaigns. Potency is nice (although 9 μM isn't impressive), but ultimately selectivity is paramount when targeting a class of proteins with well conserved binding sites, like kinases. If they found a promiscuous CDK inhibitor that happens to hit CDK20, then I bet there are tons of previously reported promiscuous CDK inhibitors that will hit CDK20 too.
Third, this paper is surprising because it exploits none of the cool new things AF2 could enable. In addition to what you mention above, the authors could have tried to counter screen (much faster in silico!), find an allosteric inhibitor, identify a PPI/complex inhibitor, or take a leap by generating a SAR series in silico and validating a few selected compounds in vitro.
Overall, this paper seems both incremental and misdirected. Saving 2 months in the discovery phase, pre-IP, is worth ~0. Not sure anyone there has much experience developing drugs. Hits are nice, but rarely the hard part. However, a hit on a protein from a structurally divergent class would be a major accomplishment.
For people who know about this area: How far away is something like this from an actual treatment? I assume its pretty far, but is it the sort of thing where you basically have to test to see if it works and is safe-ish? Or are a lot more steps involved? What does promising in this context mean? Like is it the sort of thing that has a 50% chance of eventually being useful, or is it more like 1% chance? Regardless exc…
This is about as far from actual treatment as you can be. This paper is entirely about the very first step, finding candidate molecules that bind to a specific protein.
To see how messed up things can get, take a look at Vioxx. I saw the entire life cycle of vioxx during my earlier career: from "we crystallized cox-1 and cox-2 and now we can make differential inhibitors that don't cause stomach bleeding" to clinical trials to the drug being taken off the market because the clinical trials failed to report serious problems.
So these days every time a person waves their hand and claims they solved a problem I ask what the direct and immediate effect on the actual problems will be and it also elicits an answer of "well, it's complicated... next we have to do and before we can even put it in a human". genomics with all its claims for human health went through the same hype cycle. As did nanomedical diagnostics.
Personally I think AlphaFold proved its value from day one, by firmly establishing the knowledge (which had already been speculated) that everything you need to predict protein structures is a large enough collection of protein structures, a much larger collection of protein sequence relationships, and a collection of very savvy machine learning techniques to extract the maximum information from that to produce the most physically plausible model. It also showed that you could do all this without explicitly modelling the folding process itself, which is such a huge timesaver.
For people who know about this area: How far away is something like this from an actual treatment? I assume its pretty far, but is it the sort of thing where you basically have to test to see if it works and is safe-ish? Or are a lot more steps involved? What does promising in this context mean? Like is it the sort of thing that has a 50% chance of eventually being useful, or is it more like 1% chance? Regardless exc…
I'd say this paper forms the basis for pre-clinical trials. First in-vitro with cell cultures, then in model organisms... So maybe a few years until first Human trials if they are fast? Maybe up to a decade for full approval? As to the chance nobody can say. Too many factors go into that.
Earlier quoted context omitted.
Yes, a common way to design new structures is to 'thread' the sequence onto existing folds. Rarely, there have been attempts to engineer entirely new folds, although it's not clear how necessary that is.
Early evidence seems to show that AlphaFold has trouble with single point mutations that change a protein's shape, so completely de-novo proteins will be a challenge: https://www.nature.com/articles/s41594-021-00714-2
Fortunately, evolution already encoded robustness against this sort of problem into proteins and the vast majority of single point mutations are tolerated (the resulting enzymes are often nearly as active and stable as the originals).
Earlier quoted context omitted.
I'd say this paper forms the basis for pre-clinical trials. First in-vitro with cell cultures, then in model organisms... So maybe a few years until first Human trials if they are fast? Maybe up to a decade for full approval? As to the chance nobody can say. Too many factors go into that.
there isn't data in this paper to support going to human clinical trials. micromolar inhibitors are a starting point for further molecular development, not invivo cell work.
I was sketching the path this research has to go for the benefit of a lay reader, provided nothing show-stopping occurs. The small molecule they propose may actually go all the way to Human trials, even though there is a high probability it will not turn out to do something useful.
They already used it on some kind of CDK20 activity assay. Maybe they modify it somewhat, but the next step would be to find out what it does in some kind of cell culture? I'm not an expert in drug development or this specific cancer.