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Drug Repurposing, Computed

Here’s an example of something that we’re all going to see more of in the coming years: the computational approach to biochemical pathway discovery and (potentially) new therapies. In this case, the authors are looking at some pretty intractable tumor types (type 3 and type 4 medulloblastoma), which is a good place for discovery in that anything would probably be an improvement over what we have now. These are the most heterogeneous forms of the disease (which disproportionately strikes children), and have by far the worst prognosis.

This is a drug repositioning effort, looking for new uses of known pharmaceuticals. That’s a difficult field – it’s very appealing in theory, and you hear a lot about such work, but reducing it to useful practice is much less common. In this case, the authors are taking an intensive systems biology approach. There are large databases on signaling pathways in various forms of cancer, and on the gene expression profiles of known drugs. This paper tries to bridge these with genomic data collection in medulloblastoma patients (deep sequencing, DNA copy number, DNA methylation state, mRNA expression profiling).

The computational part involves trying to model possible signaling networks based on all these data sources. And I can see how this would be a job for a machine, because the number of possibilities is huge, and examining them systematically by hand is just not feasible. The algorithm keeps adding to each possible network and trying to match these up to the expression profiles, optimizing the scoring (without, presumably, just throwing huge numbers of new members to the network just to run up the digital score!) Coupling this with the profiles of the known drugs suggested possibilities for useful off-target effects.

A good reality check is that the approach did rediscover known important signaling networks in medulloblastoma. It also binned patients into different categories based on the sequence data obtained from biopsy samples (which is particularly important in patient populations like these – just saying “Type 3 MB” makes it sound like a much more real category than it is. It also predicted a number of known chemotherapy agents as positives, which is a good sign. Overall, 12 of the top 100 drugs predicted by this method showed marked effects on medulloblastoma-derived cells in culture, which is far over what you’d expect by chance (and better than some previous efforts in this area). Interestingly, what came out the other end of this process were several cardiac glycosides, such as digoxin. It’s known from retrospective studies that patients treated with these agents seem to have lower incidence of some cancer types (although perhaps greater risk for others, although this is unclear).

These compounds had marked effects on the viability of medulloblastoma cells, with activity ranging down to nanomolar levels (!) Control cells didn’t reach those effect levels even up to 100 micromolar concentrations, so it looks like they’re on to something. This activity carried over to patient-derived tumor implant models in mice, with significant survival benefits even versus radiation therapy (up to the point of apparent cures in some animals). Now, digoxin is not the easiest thing in the world to dose, because of those well-known cardiac effects, but the blood levels involved suggest that there may be a therapeutic window. Profiling the cells involved suggests that the EKR/AKT signaling network is being affected, with increased apoptosis and midochondrial dysfunction. Interestingly, such effects turn out to have already been implicated with some of the cardiac glycosides versus melanoma tumor lines.

Now, while I like the idea of computationally repurposing drugs, I should note that digoxin had also already been discovered in 2011 as a possible prostate cancer therapy by a more traditional screen-all-the-drugs study. The current paper does not seem to reference this result. And that’s one of the drug-repurposing challenges: there are only so many approved drugs out there, and by this point many of them have been tested against many diseases.  I would be interested to see how well this sort of systems-biology method compares against straight screening (the 12% hit rate mentioned above would seem to indicate that it does have advantages). The general idea of working out important pathways by systems biology and computation has a lot of promise as well, but these are early days. I’m more interested in these prospects than that of digoxin as an antitumor agent itself, since the literature alone could have pointed someone towards that.

7 comments on “Drug Repurposing, Computed”

  1. Ed says:

    “I’m more interested in these prospects than that of digoxin as an antitumor agent itself, since the literature alone could have pointed someone towards that.”

    This is really the essential criticism of the “diseaseome”, “interactome”, and “ome-ome” approaches. What can they tell us that we don’t already know, or might not find out through other methods? I’ve tried sipping the kool-aid, but as a reality check I like to peek into the public databases to see what they can tell me about the pathways I’ve studied in the past. So far, I haven’t seen much that I can make use of. Do we really gain anything when yet another database manages to predict that the connection between MAPKK and ERK might have something to do with cancer?

    On the other hand, expert knowledge and the existing literature is completely useless when it comes to the substantial fraction of the genome which is essentially unstudied. So I still have hope that ome-ome efforts will find informative connections in the hinterlands, and I will continue checking to see if they have anything that might be useful to my research questions. Maybe I’ll find that CG12347890WTFBBQ binds to a protein of interest in a Y2H screen, and that both are highly co-expressed in a rare disease state, and that…

  2. DrZZ says:

    Good luck even getting a cardiac glycoside to the clinic as a cancer drug. It has long been known that many, many human tumor lines are inhibited or even killed at low nanomolar concentrations (see https://dtp.cancer.gov/services/nci60data/colordoseresponse/pdf/7521 for proscillaridin A data), so even hinting that the compounds are relatively specific for medulloblastoma lines is bogus. It is also well known these compounds are far less toxic to mice than other species, including humans, so activity in mouse models is a very poor indicator of possible clinical activity. I think it points out something to be aware of in these repurposing projects. Drugs that have been around for a while have been tried for many more purposes than even a careful search of the literature will find. Maybe the finding of a particular project can be considered novel because all the previous attempts have crashed and burned without leaving much of a trace because the data wasn’t considered to be worth publishing.

    1. JAB says:

      Agree entirely about cardiac glycosides being a fools errand for cancer – mice and rats are much less sensitive to them, so a xenograft can look really good, but does not model the human situation realistically.

  3. loupgarous says:

    While digoxin is indeed a much more toxic drug than usual, it’s not so toxic compared to current cancer drugs. Some biologics like everolimus (which I have personal experience with) are SO toxic you handle them with nitrile gloves prior to taking them internally (and that sounds flat absurd, but it’s what I was told to do by the hospital’s special pharmacy). I suppose the point there’s to not get it in your eyes or on other people.

    Everolimus also has a number of adverse side effects, one of which, dramatic elevation of blood glucose as a consequence of one mechanism of action, inhibition of glycolysis, caused me to stop using it. Trust me, if digoxin had activity against my tumors, I’d have given it a try.

    Thalidomide and its derivatives are examples of drugs infamous for their toxicity to the fetus, but the same metabolic activity makes them valuable in managing damage from leprosy and treating cancer. It’s a notable, even drastic example of repurposing drugs (FDA had to relax its ban on thalidomide to permit Hansen’s Disease sufferers to use it).

  4. Is it odd they indicate the same three cardiac glycosides as this ALS iPSC modeling paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4772428/pdf/nihms761274.pdf
    (page 5, last paragraph b/f Discussion)

  5. SSG says:

    Drug re-purposing has another angle to it: to make out-of-patent molecules alive again.

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