CoVid-19: challenges and responses in simulation, modeling and beyond - 3rd CECAM Webinar

In this series of webinars, CECAM offers insights on current initiatives and provide an opportunity to learn and discuss general aspects of the problem and more specific projects. Four e-meetings are currently planned for April 21st and 28th, May 5th and 12th. You can find more information on this initiative here.

This third chapter will be livestreamed on Youtube on May 5th, at 15:00 CEST. 

Program:
15:00   Welcome and Introduction, Ignacio Pagonabarraga (CECAM)
15:10   Andrea Cavalli (Università di Bologna and IIT Genova): Current therapeutic options for CoVid-19: prediction of mechanism of action through atomistic simulations and machine learning
15:40   Coffe-e-break
15:50   Modesto Orozco (the Institute for Research in Biomedics, Barcelona): HPC and BigData approaches in Covid19 research
16:20   Questions and Answers

Abstracts:

Current therapeutic options for CoVid-19: prediction of mechanism of action through atomistic simulations and machine learning
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated coronavirus disease 2019 (CoVid-19) has gripped the world in a pandemic, challenging its healthcare infrastructure, economy, and culture.Major efforts are underway to quickly identify therapeutic options to treat and prevent the spread of CoVid-19. A three-stage classification for CoVid-19 has recently been proposed, with increasing severity that corresponds to distinct clinical findings, responses to therapy, and clinical outcomes. The initial stage occurs at the time of the virus inoculation and early establishment of the disease. In the second stage, an established pulmonary disease can be observed with viral multiplication and inflammation usually localized in the lung.
The third stage usually shows an extra-pulmonary systemic hyperinflammation syndrome, resulting in adecrease in helper, suppressor, and regulatory T cell counts. Different therapeutic options must be considered for the different stages of the disease, with targets at the virus particles or within the human cells that should be carefully selected and properly modulated. In stage I and IIA, antiviral compounds should be considered, whereas anti-inflammatory drugs and immunomodulators are the best option for stages IIB and III. In this talk, we discuss the current therapeutic options and target selection for the different stages of CoVid-19, with particular focus on computational studies able to in depth rationalize and predict mechanism of actions and point to the most promising drugs from the FDA- (or EMA-) approved medicines. Finally, a study based on machine learning will be presented, which was able to discover promising pathways and targets pointing also to polypharmacology mechanism of action as an innovative way to target current and
future coronavirus outbreaks.

HPC and BigData approaches in Covid19 research:
At a time where we though major threats for human health would never come from infection diseases, COVID19 has showed has how fragile we are, both as individuals and as a society to infections originated from viruses that were kept cryptic in exotic animals for centuries. Virus emergences hit us with an expansion rate faster than our ability to find cures and generates a sense of panic in the society. COVID19 is being a nightmare, but we cannot neglect a few positive aspects in the current situation, one of them, the impressive multidisciplinary response of the scientific community that is not only focus on finding treatments, but that is sharing all the information. Molecular simulation techniques are become crucial in the multidisciplinary approach to fight COVID19 and thousands of groups around the world are running simulations on COVID19 systems, both in local computers and in high performance computer centers which are prioritizing COVID19 research. I will comment a couple of initiatives done in Barcelona to: i) optimize the use of HPC resource to COVID19 research and ii) optimize the way in which the structural information on COVID19 system is stored, analyzed and shared.