Researchers have developed a screening tool for discovering unexpected effects that drugs may have on living cells. It could provide a better way of identifying both potential side effects of and applications for new drugs – and take the serendipity out of the drug discovery process.
Published in the current issue of the journal Nature Chemical Biology, the new tool combines modern chemical screening techniques with computer analysis. Using it, pharmaceutical companies could get an early snapshot of the potential uses and possible side effects of particular drugs, says Stephen Michnick, who heads a laboratory at the Department of Biochemistry at the University of Montreal. One of the primary researchers on the project, Michnick developed the technique with John Westwick, president and chief scientific officer at Odyssey Thera in San Ramon CA, which specializes in using mass-screening techniques in drug discovery.
Most drugs work by interacting with target proteins to influence their effect on biochemical pathways within cells. But because these pathways and their interactions are complex, a drug can often have side effects – beneficial or toxic. To ferret out these effects, drugs nowadays are usually screened one target protein at a time, says Graeme Milligan, a molecular pharmacologist at the Institute of Biomedical and Life Sciences, University of Glasgow. Although it works, this approach can be costly for the pharmaceutical industry. “Potentially toxic and off-target effects are generally not discovered until a later stage,” he says, after a lot of time, money, and effort have been spent.
Instead, by profiling and comparing more than 100 known drugs, this latest research showed that many drugs could be grouped based on the way they influenced cells – rather than on their structure or the proteins they were targeting. Using this methodology, the researchers profiled the antidepressant sertraline, for example, showing that its profile for certain biochemical pathways was similar to many anti-cancer drugs, says Michnick.
The research analysed the way individual pairs of proteins interact in healthy cultured cells, by introducing engineering proteins that would bind to each pair and glow whenever they interact. The scientists were then able to use automated screening techniques to measure these interactions and where in the cell they occurred.
By comparing the normal responses of these pairs of proteins with those exposed to a particular drug, they built a picture of how that drug influenced the stages of each biochemical pathway. The researchers then used a simple computer model to categorize the drugs according to how they influenced these pathways. This allowed them to compare and ultimately predict the overall effect each of these existing drugs would have on cells. For example, four existing drugs currently not used for treating cancer were found to be grouped together with cancer-inhibiting drugs, suggesting that they had similar effects on inhibiting cancer growth, which was later verified.
Although the pathways used in this experiment focused on anti-cancer therapies, it’s possible to screen for other effects, says Michnick. By carrying out profiles of toxins and known failed drugs, it should be possible to find other known and unknown effects.
This whole process is much like creating functional “fingerprints” of drugs, says Jim Wells, a biochemist at the Department of Pharmaceutical Chemistry at the University of California, San Francisco, and president of Sunesis Pharmaceuticals. What’s more, such computational analysis techniques are becoming increasingly popular, says Stephen Muggleton, head of the Computational Bioinformatics Laboratory at Imperial College London. Although computer algorithms have been used in drug discovery for decades, he says, they’ve primarily been statistical techniques. And more recently there’s been a move toward more sophisticated ways of targeting specific protein interactions.
Michnick agrees that his technique involves different existing tools. “What is unique about this approach is that we are doing it on living cells,” he says. This has an advantage over tests on isolated proteins: it’s possible to identify not just the rate of a response, but where it might occur in a cell. Knowing whether interactions happen close to the nucleus or the cell membrane can help to clarify the mechanisms involved, he says, and ultimately help enhance the drug discovery process.
The hype around DeepMind’s new AI model misses what’s actually cool about it
Some worry that the chatter about these tools is doing the whole field a disservice.
The walls are closing in on Clearview AI
The controversial face recognition company was just fined $10 million for scraping UK faces from the web. That might not be the end of it.
A quick guide to the most important AI law you’ve never heard of
The European Union is planning new legislation aimed at curbing the worst harms associated with artificial intelligence.
These materials were meant to revolutionize the solar industry. Why hasn’t it happened?
Perovskites are promising, but real-world conditions have held them back.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.