New Machine Learning Technique Quickly Analyzes Nanomedicines for Cancer Immunotherapy

SNAs are forms of DNA and RNA in the form of beads arranged on the surface of a nanoparticle. Credit: Chad Mirkin / Northwestern University

  • Spherical nucleic acids are a class of personalized drugs for the treatment of cancer and other diseases.
  • SCNs are difficult to optimize because their structures can vary in many ways
  • The team at Northwestern University has developed a library and machine learning approach to quickly synthesize, analyze, and select powerful SNA drugs.

EVANSTON, Ill. – Because of their ability to treat a wide range of diseases, spherical nucleic acids (SNAs) are poised to revolutionize medicine. But before these digitally designed nanostructures can reach their full potential, researchers must optimize their various components.

A Northwestern University team led by nanotechnology pioneer Chad A. Mirkin has developed a direct method to optimize these difficult particles, bringing them closer to a viable treatment for many forms of cancer. genetic diseases, neurological disorders and more.

"Spherical nucleic acids represent a new class of exciting drugs that are already part of five clinical trials in humans for the treatment of diseases, including glioblastoma (the most common and deadly form of brain cancer)." and psoriasis, "said Mirkin, the inventor of SNA and B. Rathmann Professor of Chemistry at Weinberg College of Arts and Sciences Northwestern.

A new study published this week in Nature Biomedical Engineering details the optimization method, which uses a library approach and machine learning to quickly synthesize, measure and analyze the activities and properties of SCN structures. The process, which examined more than 1,000 structures at a time, benefited from the SAMDI-MS technology, developed by Milan Mrksich, co-author of the study, Professor Henry Wade Rogers in biomedical engineering at the McCormick Northwestern School of Engineering and the Director of the Center for Synthetic. Biology.

Invented and developed at Northwestern, SCNs are nanostructures made up of DNA and RNA in the form of beads arranged on the surface of a nanoparticle. Researchers can digitally design their SCNs to be precise and personalized treatments that cut genes and cell activity, and more recently, vaccines that stimulate the body's immune system for the treatment of diseases, including some forms of cancer.

SCNs have been difficult to optimize because their structures, including particle size and composition, DNA sequence, and the inclusion of other molecular components, can vary in many ways, has an impact or increased effectiveness on triggering an immune response. This approach revealed that structural variations lead to biological activities showing non-obvious and interdependent contributions to the effectiveness of SCNs. Since these relationships had not been predicted, they would likely have gone unnoticed in a typical study of a small set of structures.

For example, the ability to stimulate an immune response may depend on the size, composition, and / or manner in which the DNA molecules are oriented on the surface of the nanoparticles.

"With this new information, researchers can classify structural variables in order of importance and effectiveness and help establish design rules for the effectiveness of the SNA," said Andrew Lee. assistant professor in chemical and biological engineering at the McCormick School of Engineering. author.

"This study shows that we can address the complexity of the SNA's design space by allowing us to focus on and exploit the most promising structural features of SCNs and, ultimately, develop powerful treatments. cancer, "said Mirkin, also director of the International Institute for Nanotechnology.

the Nature Biomedical Engineering This document is entitled "Addressing the Complexity of Nanomedicine with Screening and New High-Speed ​​Machine Learning". Other co-authors are Neda Bagheri, Gokay Yamankurt, Eric J. Berns and Albert Xue, Northwestern University.

Explore further:
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More information:
Gokay Yamankurt et al, Exploration of Nanomedicine Design Space with High Throughput Screening and Machine Learning, Nature Biomedical Engineering (2019). DOI: 10.1038 / s41551-019-0351-1

Journal reference:
Nature Biomedical Engineering

Provided by:
Northwestern University

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