Researchers have developed a machine-learning algorithm to help to automate high-throughput screens of epigenetic machines.
FREMONT, CA: The incredible capacities of Machine Learning (ML) have already revolutionized the way doctors treat and diagnose diseases. Presently, it is also transforming the way scientists are discovering new medications. Recently, an ML algorithm has been developed by researchers at Sanford Burnham Prebys Medical Discovery Institute to collect information from microscope images, thus empowering high-throughput epigenetic drug screens to reveal new diagnoses for heart disease, cancer, mental illness and more.
For identifying a rare handful of drug candidates that influence desired epigenetic effects, researches looked for new techniques to screen hundreds of potential compounds. The study explains the powerful image-based method that allows high-throughput epigenetic drug discovery.
Epigenetics are chemical labels on DNA that alter gene expression. The cell’s epigenetic state shows all the changes in the cell, including response to a drug or environmental stress. Numerous medicines targeting epigenetic alterations have already been approved by the U.S. Food and Drug Administration (FDA) for cancer treatment. However, the growth of drug discovery has slowed down due to the lack of a high-throughput screening technique. Scientists presently visualize epigenetic changes utilizing special dyes and conventional microscopy strategies.
In the study, an ML algorithm was trained by scientists utilizing a set of beyond 220 drugs known to react epigenetically. The resulting technique, called Microscopic Imaging of Epigenetic Landscapes (MIEL), can detect active drugs, characterize the compounds by their molecular function, find epigenetic changes across numerous cell lines and drug concentrations and help detect how unfamiliar compounds react. The researchers utilized the technique to identify epigenetic compounds that can help in treating glioblastoma, a fatal brain cancer.
The pharmaceutical firms in search of epigenetic drug screens can immediately use it. Besides, the technique will benefit industry and academic researchers working on mechanistic studies as it helps to identify and categorize epigenetic changes caused by genetic changes, experimental treatments, or other methods.
This algorithm is already applied by Terskikh and his team to study epigenetic changes in aging cells to develop compounds that will facilitate healthy aging. The work is being conducted in amalgamation with Sanford Burnham Prebys professor Peter Adams, Ph.D. Terskikh is also very excited to widen the technology from 2D images to 3D videos for extending the power of the approach.