by Angela Guess
Chris Wood recently wrote in GizMag, “Testing out newly developed drugs is an extremely time-consuming process, and it can be difficult to get right. Now, a team of scientists at Carnegie Mellon University (CMU) is working to streamline the task, creating a robotically-driven experimentation system that’s able to reduce the number of tests that have to be carried out by as much as 70 percent. When working on a new drug, scientists have to determine its effects to ensure that it’s both an effective treatment and not harmful to patients. This is hugely time-consuming, and it’s simply not practical to perform experiments for every possible set of biological conditions.”
Wood goes on, “That’s where CMU’s new robotic system steps in. It uses a machine learning approach to choose which experiments to conduct, using patterns in the data to accurately predict results of experiments without actually carrying them out. The system is able to conduct selected experiments on its own, using liquid-handling robots and an automated microscope. Its abilities were put to the test in a study to determine the effects of 96 drugs on 96 cultured mammalian cell clones, containing different, fluorescently-tagged proteins. A total of 9,216 experiments were possible, each of which involved testing the effects of a drug by taking a picture of it mixing with the target cell.”
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