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New machine learning technique rapidly analyzes nanomedicines for cancer immunotherapy

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"Spherical nucleic acids represent an exciting new class of medicines that are already in five human clinical trials for treating diseases, including glioblastoma (the most common and deadly form of brain cancer) and psoriasis," said Mirkin, the inventor of SNAs and the George B. Rathmann Professor of Chemistry in Northwestern's Weinberg College of Arts and Sciences. A new study published this week in Nature Biomedical Engineering details the optimization method, which uses a library approach and machine learning to rapidly synthesize, measure and analyze the activities and properties of SNA structures. The process, which screened more than 1,000 structures at a time, was aided by SAMDI-MS technology, developed by study co-author Milan Mrksich, Henry Wade Rogers Professor of Biomedical Engineering in Northwestern's McCormick School of Engineering and director of the Center for Synthetic Biology. Invented and developed at Northwestern, SNAs are nanostructures consisting of ball-like forms of DNA and RNA arranged on the surface of a nanoparticle. Researchers can digitally design SNAs to be precise, personalized treatments that shut off genes and cellular activity, and more recently, as vaccines that stimulate the body's own immune system to treat diseases, including certain forms of cancer.