Maharjan, Sushila

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

AbstractConvergence of life sciences, engineering, and basic sciences has opened new horizons for biologically inspired innovations, and a considerable number of organ‐on‐a‐chip platforms have been developed for mimicking physiological systems of biological organs such as the brain, heart, lung, kidney, liver, and gut. Various biophysicochemical factors can also be introduced into such organ‐on‐a‐chip platforms to study metabolic and systemic effects spanning from drug toxicity to different pathologic manifestations. There is also a pressing need to develop better disease models for common pathologies using variations of these platforms. This can be achieved by recapitulating the unique microenvironment of a disease to investigate the cause and development of abnormal conditions as well as the structural and functional changes resulting from such a pathology. In this review, the organ‐on‐a‐chip platforms that have been developed to model different pathologies of neurodegenerative, cardiovascular, respiratory, hepatic, and digestive systems, along with cancer are summarized. Although the field is still in its infancy, it is anticipated that developing disease model‐on‐a‐chip platforms will likely be a valuable addition to the field of disease modeling, pathology studies, and improved drug discovery.