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Drug Assays

Sorting Out Potential Antibiotics

One of the tricky parts about trying to find new antibiotics is that many screening modes will just discover things that have been discovered before. You’d think that if you’re looking for “bug killers” that you could just run through the compound collection looking for stuff that, well, kills bugs, but the problem is that everyone has done that before. For decades. So unless your compound collection is pretty unusual, odds are that you’re going to hit things that others have already hit. (Odds are as well that you’re going to hit things that will kill off mammalian cells at the same rate as bacterial ones, too, but that’s another counterscreen!) There may well be some interesting compounds in there, but how do you know which ones those are?

High on the order of business, then, is figuring out how your hits are working so you can prioritize the more interesting (new) stuff over the well-trodden pathways. That is not so easy. This new paper, though, suggests a high-throughput way to sort such things out in the case of Mycobacteria. The authors are a multinational team from the ETH-Zürich, the Hungarian Academy of Sciences, the Institut Pasteur, the Swiss Tropical and Public Health Institute, the University of Basel, and GSK’s site in Spain, and there are certainly plenty of people on that list who know anti-infectives research. What they’re illustrating is a comprehensive metabolic profiling technique that allows potential antibiotic compounds of unknown mechanism to be narrowed down.

The first step was to build a reference set of metabolic data in M. smegmatis, using 62 known compounds that cover 17 different mechanisms of action. The bacterial broth medium was extracted directly at various time points and treatment concentrations, and shoveled right into a mass spec instrument. This generated a pretty ferocious pile of data, which was cleaned up by eliminating drug-specific ions and the like, and standardized across different plates and extractions to try to get a workable baseline. About 5% of the detected metabolites in the broths showed real changes on drug treatment, in the end. The time courses of these changes made sense – drug targeting metabolic processes directly showed up quickly, while things like protein synthesis targets took longer to alter the profiles.

Correlating the pathways, enzymes involved, and mechanisms of action (no small feat) suggested that MoAs could indeed be binned by looking at the metabolite profiles (even if they weren’t directly targeting a metabolic pathway). Some of them were firmer or less variable across different compounds (such as the quinolones) than others (cell wall synthesis inhibitors), but they all did end up with distinguishable signatures. The next test was to assay a list of 212 compounds that had been identified by the GSK team as active against Mycobacteria but whose mechanisms were unknown.

This picked out a number of inhibitors of DNA replication and folate biosynthesis (both of which mechanisms are broadly covered by existing antibiotics, although specific enzymes in these pathways can presumably still be new targets). One compound, GSK2623870A, appears to work in the fatty acid biosynthesis area (which Mycobacteria spend a good amount of metabolic energy on), and a total of six similar compounds were identified that nonetheless still showed somewhat varying metabolome profiles.

The belief is that this sort of profiling should translate to other varieties of bacteria – if it does, it also has ten-to-hundredfold higher throughput than the existing profiling methods. No libraries of mutant bacteria are needed (a known way of sorting such questions out). The culture conditions used for this sort of assay, though, will probably have to be investigated further, since there are surely some genes (and whole pathways) that are activated under some conditions and not others. Finding protocols that mimic the natural infection process as closely as possible will improve the chances of success. The authors propose moving on to those different bacterial families and expanding the list of reference compounds, since this study seems to have worked out, and trying to turn this into a general mechanism-clarifying platform for antibiotic discovery.

That’s going to generate mighty piles of data, but (as is often the case), this is a feature, not a bug. As the authors mention, it will be interesting to turn various machine-learning algorithms loose on these data sets to see what they can make of it. If things work out, you could also end up predicting new combination therapies as well, if you can match up complementary pathways, which could open the door to using more bacterial-nonlethal compounds that never would have been picked up in solo testing. Here’s hoping it works!

13 comments on “Sorting Out Potential Antibiotics”

  1. Emjeff says:

    Wow, GSK’s Tres Cantos site has actually produced something after more than 10 years? Amazing…

  2. Thoryke says:

    Aside from wanting them to succeed because we need new antibiotics, I now want them to succeed because it would reward SOMEONE for playing the long game rather than churning for quick profits….

  3. MikeRobe says:

    Anyone know what this Wonder compound looks like? Curious minds want to know.

    There are many fatty acid synthesis inhibitors known, the dreaded Triclosan is one, destined to the dustbin of Forbidden Molecules by the FDA if they ever can shoot straight.

    So where’s the MIC data on this GSK compound as well? Surely, Tres Cantos has bacterial strains that can show what they are really worth and that would of been the follow up experiment to their metabolomic methods.

  4. Uncle Al says:

    When nothing works, do it the other way. Given an olefin, generate some diimide and saturate it. Is that better (avermectin, ivermectin)? But nobody does that! Do it to everything. DOI:10.1002/jlac.18922710120 (1892). That’ll save you a day.

    Carbene to make cyclopropanes, et al. CH2I2, Zn, TMSCl, CuCl, Et2O. Of course it is scut work (automate) – but everything else is exhausted. Do not dismiss the awesome if fickle powers of mistaken assumptions, luck, fetish, and transient frank stupidity.

  5. Some guy says:

    I used to work at GSK and my recollection was that they had tossed their antibiotic group up in Philly around the same time they got rid of half of us down in RTP. I had heard rumors that the antibiotic group was able to find funding from outside to allow them to continue, DARPA I believe. So when you write, “The next test was to assay a list of 212 compounds that had been identified by the GSK team as active against Mycobacteria but whose mechanisms were unknown”, I am wondering if this was from a historical screen or a more current screen. I wasn’t under the impression that GSK worked in antibiotics (or much of anything else) any longer. (Tres Cantos was a HTS site when I was there.).

    I do like this approach you’ve described. One quick question, you mention in the first paragraph about testing the toxicity in mammalian cells which I think appropriate. However, when I used to read the literature disclosing new compounds with unknown mechanism of actions (usually from academics), they never did this or if they did they did not disclose it. Did they do this on their set in this report? (Sorry, paywall.) Just because they have a novel MOA doesn’t make them non-toxic. So I am curious what would be the most appropriate cell line(s) for that type of screen. Are there companies out there that will run a panel of tox screens? What are the cell lines that would be most relevant?

    1. Mister B says:

      I have some friends in the academic working on antibiotic or anti-betalactamase (same area roughly). In the academic, the main limite in the toxicity tests in mammalian cells is the cost of those. Simply.

      And also, it’s a huge gamble. Can you publish a new serie of compounds “effective antibiotics that also kill the patient” ? 🙂

  6. DrOcto says:

    The science equivalent of the property mantra ‘location location location’ is ‘data data data’.

    Now, if only I could ensure that a third of my data wasn’t so terrible….

  7. Me says:

    Hmmm

    Like the paper. Like the idea – obvious caveat being how close the metabolic pathways in the bugs are to human.

    Regarding questions of how/when antibac data was generated at GSK, Tres Cantos has been running malaria/TB programs for years and scooped large pots of cash from places such as Gates Foundation to do so, so this is no surprise to me.

    1. Mol Biologist says:

      Hmmm I do like the technical part of paper. There are a lot of modern techniques and a lot of work was done. However, IMO there are few things are missing. First, if you really point the MoAs you could indeed be able to pin the compound which able to overcome antibiotic resistance. I do like Kim Lewis approach because his unique selection process is targeting most vulnerable part of bacteria metabolism.
      https://www.nature.com/news/promising-antibiotic-discovered-in-microbial-dark-matter-1.16675
      Second, the metabolite profiles similarity and transcriptional activities. IMO it can be just a redundancy in response to chemical intervention: The key to protection of organism integrity and cannot be simply interpreted as MoAs.
      We do know a lot about remote preconditioning, some drugs which can activate transcription pathways but still know a little about the mechanism. http://journals.sagepub.com/doi/abs/10.1177/1074248411409040

  8. Matthew K says:

    I don’t want to know where M. smegmatis was cultured from.

  9. bacillus says:

    For MTB, they’ll not only have to show lack of toxicity for mammalian cells, but also penetrability into them, since this is a facultative intracellular pathogen.

  10. Mike Robe says:

    So where’s the structure? Been waiting and waiting Tres Cantos. You all need to test your compounds in macromolecular synthesis assays, looking at protein, nucleic, DNA and cell wall synthesis. You hit one it could be drug, you hit 2 or more its a membrane perturbation agent and you should move on.

    No structure means its probably junk.

  11. Katherine's twitch says:

    GSK2623870A comes directly from GSK’s decade long bacterial topoisomerase program. That sucker with the amino piperidine linker is gonna light up the hERG assay like the 4th of July. On another note, I have been to the Tres Cantos site…not much ever came out of there but the lunchroom does serve very good paella!

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