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Cvitkovic, M.

Publications and source records attributed to Cvitkovic, M..

2 recordsLinked to original sources

Long-term moisture barrier performance of liquid crystal polymer for implantable medical electronics

1.Liquid crystal polymer (LCP) is commonly used in the electronics industry due to its favorable dielectric, thermal, and insulative properties. It has recently gained popularity in the medical field for these same reasons, as well as its biocompatibility, moisture barrier properties, and ability to be microfabricated into thin film flexible circuits or flex PCBs. While polymers such as polyimide and Parylene C remain more common for electronics encapsulation and flexible circuit fabrication due to their relatively lower barriers to adoption and history of use, LCPs superior moisture barrier performance and low risk of delamination make it a promising material for chronic use in medical devices. In this work, the moisture barrier properties of LCP are evaluated using in vitro accelerated aging over 59-61 weeks at 65-68 {degrees}C, corresponding to an equivalent implanted lifetime of 8.1 and 9.4 years at 37 {degrees}C for each of two sample groups: LCP as an electronics encapsulant and as a flexible circuit substrate. In the encapsulation group, relative humidity inside an encapsulation pocket was monitored over time with no noticeable change in humidity throughout the measurement period. In the flexible circuit group, impedance of laminated interdigitated electrodes was monitored over time, with an average decrease to 44% of the initial impedance value across all successful samples due to the moisture absorption of the LCP, which has remained stable for the latter half of testing. In both groups, no delamination was observed. These findings demonstrate that LCP is a viable moisture barrier for electronics in implanted medical devices for an estimated equivalent lifetime of at least 8.1 years.

bioengineering↗

COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

We report the results of the COVID Moonshot, a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a non-covalent, non-peptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

biochemistry↗