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High-Speed Screens, A.I., Boost Nanoscale Medicines

Spherical nucleic acid illustration

Spherical nucleic acid illustration (Northwestern University)

20 Feb. 2019. Biomedical engineers are using high-throughput screening techniques and machine learning to speed and improve the design of new drugs based on nanoscale particles. An interdisciplinary team from Northwestern University in Evanston, Illinois describes their techniques in the 18 February issue of the journal Nature Biomedical Engineering (paid subscription required).

Researchers from Northwestern University’s medical school and engineering and chemistry departments are seeking better ways to design and discover drugs that address highly specific targets for diseases such as cancer, neurological disorders, and autoimmune conditions. Chad Mirkin, director of International Institute for Nanotechnology on the Northwestern campus and one of the project leaders, is a pioneer in developing spherical nucleic acids as therapeutics. The new research aims to make the discovery and design of spherical nucleic acids more accessible and efficient.

Spherical nucleic acids are densely packed particles, with nucleic acids like those in the genetic materials DNA and RNA, around a spherical core. Their nanoscale size and shape, say the researchers, enable the particles to efficiently enter cells, and once inside regulate genetic processes of those cells. Spherical nucleic acids can also be programmed to assemble like building blocks into more complex structures. As reported by Science & Enterprise in May 2017, a clinical trial is testing a spherical nucleic acid treatment for glioblastoma, cancer that forms in the brain’s glial cells.

But as the authors note, spherical nucleic acids and other new drugs using nanoscale particles — where 1 nanometer equals 1 billionth of a meter — are not being widely explored, due to their complexity, and lack of synthesis and analysis tools. The new research identifies tools to evaluate and design spherical nucleic acids, which the authors say can be applied to other nanoparticle-based drugs. One of those tools is a type of mass spectrometry, an optical analytical method to determine the composition of chemical substances, called self-assembled monolayer desorption ionization, or SAMDI, developed by co-senior author Milan Mrksich, a professor of biomedical engineering and chemistry. SAMDI speeds the analysis and identification of chemical reactions at the molecular level.

The team first identified some 1,000 spherical nucleic acids based on 11 design factors, then submitted these candidates to analysis by SAMDI. The SAMDI analysis looked specifically for the ability of spherical nucleic acids to invoke an immune response, a key factor in immunotherapy drugs, like many new treatments for cancer. The results found previously unknown and interdependent variations in the structure of some spherical nucleic acids that could trigger immune responses and other biological reactions.

The team then used a machine learning algorithm, a form of artificial intelligence, to model immune-system activation by spherical nucleic acids. This modeling step identified the minimum number of spherical nucleic acids, or SNAs, with a given structure needed to generate the desired biological outcomes. “This study,” says Mirkin in a university statement, “shows that we can address the complexity of the SNA design space, allowing us to focus on and exploit the most promising structural features of SNAs, and ultimately, to develop powerful cancer treatments.”

Both Mirkin and Mrksich are scientific founders of companies in or near Chicago to commercialize their discoveries. Mirkin in 2011 started the company Exicure Inc. to develop spherical nucleic acid drugs to invoke the immune system and regulate gene expression against a wide range of disease targets. That same year, Mrksich founded SAMDI-Tech Inc. to apply his technology to high-throughput screening for commercial drug discovery and design.

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