Flashback fifty years ago and many of the modern world’s inventions and discoveries would have been undreamed of – including using machines to discover new anti-cancer drugs. But that’s exactly what’s happening. New research published in eLife by scientists at Sanford Burnham Prebys Medical Discovery Institute details how an algorithm is helping to develop epigenetic drug screens and the potential that this technology has for cancer and other diseases.
Senior author Alexey Terskikh, Ph.D. is an associate professor in Sanford Burnham Prebys' Development, Aging and Regeneration Program. Terskikh commented on the study saying, "In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds. Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery."
The image-based approach Terskikh is referring to is Microscopic Imaging of Epigenetic Landscapes (MIEL), a type of high-content phenotypic screening that is more advanced than past technologies in its ability to, as the authors write, “capture the nuclear staining patterns of epigenetic marks and employ machine learning to accurately distinguish between such patterns.”
Is all that sounding a little complicated? Let’s break it down in simpler terms by first grasping the concept of epigenetics. Watch the video below to understand how epigenetics works.
A little clearer now? So, what the researchers in Terskikh’s lab did was take advantage of a machine-learning algorithm to track the epigenetic changes in cells for more than 220 drugs, in order to determine just how the compounds are working. Through this process, they were able to identify certain drugs that can target and potentially treat glioblastoma, the most fatal kind of brain cancer. They say this method could be useful in identifying compounds that could treat other types of cancer, as well as heart disease and mental illness.
The researchers are hoping to put their algorithms into the commercial field as soon as possible. "Our method is ready for immediate use by pharmaceutical companies looking to develop epigenetic drug screens," says first author Chen Farhy, Ph.D., a postdoctoral researcher in the Terskikh lab. "Industry and academic researchers working on mechanistic studies may also benefit from this method, as the algorithm can detect and categorize epigenetic changes induced by experimental treatments, genetic manipulations or other approaches."