COVID19 Projects
Understanding the COVID-ASSOCIATED Clotting Disorder
One of the health conditions contributing to the critical illness of COVID19 patients is the disruption of the blood clotting system. The effects range from microclots blocking small blood vessels that reduce oxygen and nutrient supply and thus organ function, to macroclots (embolism) that suddenly block lung and body arteries and may be lethal.
The pathogenesis of this disorder is still not fully understood, hence, rational therapies that address the underlying cause(s) have yet to be developed.
Our current research analyzes the (dys)regulation of the clotting system in COVID19 patients.
Publication: von Willebrand Factor Multimer Formation Contributes to Immunothrombosis in Coronavirus Disease 2019. A. Doevelaar et al. Critical Care Medicine: May 2021 - Volume 49 - Issue 5 - p e512-e520. doi: 10.1097/CCM.0000000000004918
Image credit: NIH-NIAID / Wikipedia.org
Plant Natural Products as Antivirals
Numerous phytochemicals, or plant chemicals, have antiviral acticity: the USDA Phytochemical and Ethnobotanical Databases lists 343 compounds with antiviral activity. Furthermore, phytochemicals can be used in a polypharmacological approach that inhibits multiple viral proteins at once and makes escape through mutations less likely.
Finding the right phytochemicals for viral protein inhibition is challenging, however, in-silico screening methods make this problem more tractable. In this study, we screened 272 anti-viral phytochemicals against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow.
In a structure-based virtual screening (SBVS) the initial phytochemical library was docked against the SARS-CoV-2 protein structures. Subsequently, chemical features of 34 lead compounds were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. Computational docking validation showed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Thus, incorporating machine learning elements into a virtual screening workflow enhances the discovery process.