3 ms·

Decades ago when I worked in a lab, "high-throughput screening" was definitely on the list of buzzwords. Given what I saw back then, I'm struggling to understa

Decades ago when I worked in a lab, "high-throughput screening" was definitely on the list of buzzwords. Given what I saw back then, I'm struggling to understand how there could possibly be "a library [..] of 6,111 molecules at various stages of investigation for human diseases" (a.k.a. "Drug Repurposing Hub") which hasn't already been partially or fully screened for interesting antibiotic activity. Could it be there's more fame (and funding) in a project where you can publish a paper and get headlines about "ML" and "superbugs", than in actually testing a library of existing compounds to see if any of them kill E. coli?

anonsivalley652 7y ago
For DD, it seems initial screening of as many phages, microbes and compounds as possible using highly-automated brute force might be plenty efficient to test their effectivenesses against every horrible, resistant and opportunistic pathogen for candidate identification. Maybe flying drones out to collect samples in as many random places (public places, restrooms, dirt and even more random places) as possible, generating more samples than a team of humans ever could. There doesn't seem to be any "One True Way," but a holistic synthesis of collection, identification and selection methods.
- logifail 7y ago
  > highly-automated brute force might be plenty efficient Hasn't this been going on in one form or another for many decades? When I was in this field (20+ years ago) I got to visit labs at Glaxo Wellcome, SmithKline Beecham, Zeneca and so on. Even back then they were proudly showing off lab robots which allowed them to run large-scale screening experiments. Not sure any of this stuff is quite as revolutionary as it looks.
  - kokey 7y ago
    I did a stint at GSK about 13 years ago. The large scale mechanised experiments also produced big databases of compounds and their properties and mechanisms to make these accessible to researchers and build bigger clusters of systems to run models on. There was a lot of talk of ML but implementation was nowhere near the scale we see nowadays. I think what has changed significantly is the scale, second to that the methods including algorithms and methods to feed back real data into it. It's an evolutionary improvement but I sense it's one of those things where one lucky step in the evolution could make a big difference in productivity.
    - logifail 7y ago
      > one lucky step in the evolution could make a big difference in productivity There's also the issue of ROI. Is Big Pharma really expecting to find an antibiotic blockbuster drug? 20+ years ago there was a distinct lack of excitement around antibiotics in general, at least from the commercial types. Q: Is there more expectation/excitement/R&D budget now?
  - anonsivalley652 7y ago
    I think you may have missed my point. Human labor of 10 workers costing $ 1+ million USD in total OpEx isn't as scalable as sending out 10,000 drones to collect samples. Drones that can return in 45 minutes, don't need lunch breaks, and don't have to find parking. Industrial-scale sample processing -> putting more people a little farther down the pipeline to actually look at what's interesting rather than doing unnecessary/low-skill field work.

anonsivalley652 7y ago

For DD, it seems initial screening of as many phages, microbes and compounds as possible using highly-automated brute force might be plenty efficient to test their effectivenesses against every horrible, resistant and opportunistic pathogen for candidate identification. Maybe flying drones out to collect samples in as many random places (public places, restrooms, dirt and even more random places) as possible, generating more samples than a team of humans ever could. There doesn't seem to be any "One True Way," but a holistic synthesis of collection, identification and selection methods.

logifail 7y ago
> highly-automated brute force might be plenty efficient Hasn't this been going on in one form or another for many decades? When I was in this field (20+ years ago) I got to visit labs at Glaxo Wellcome, SmithKline Beecham, Zeneca and so on. Even back then they were proudly showing off lab robots which allowed them to run large-scale screening experiments. Not sure any of this stuff is quite as revolutionary as it looks.
- kokey 7y ago
  I did a stint at GSK about 13 years ago. The large scale mechanised experiments also produced big databases of compounds and their properties and mechanisms to make these accessible to researchers and build bigger clusters of systems to run models on. There was a lot of talk of ML but implementation was nowhere near the scale we see nowadays. I think what has changed significantly is the scale, second to that the methods including algorithms and methods to feed back real data into it. It's an evolutionary improvement but I sense it's one of those things where one lucky step in the evolution could make a big difference in productivity.
  - logifail 7y ago
    > one lucky step in the evolution could make a big difference in productivity There's also the issue of ROI. Is Big Pharma really expecting to find an antibiotic blockbuster drug? 20+ years ago there was a distinct lack of excitement around antibiotics in general, at least from the commercial types. Q: Is there more expectation/excitement/R&D budget now?
- anonsivalley652 7y ago
  I think you may have missed my point. Human labor of 10 workers costing $ 1+ million USD in total OpEx isn't as scalable as sending out 10,000 drones to collect samples. Drones that can return in 45 minutes, don't need lunch breaks, and don't have to find parking. Industrial-scale sample processing -> putting more people a little farther down the pipeline to actually look at what's interesting rather than doing unnecessary/low-skill field work.