Andrea Cavalli - University of Bologna and IIT Genova
Current therapeutic options for CoVid-19: prediction of mechanism of action through atomistic simulations and machine learning

Modesto Orozco - Institute for Research in Biomedicine, Barcelona
HPC and BigData approaches in CoVid-19 research

Tuesday May 5 2020

Andrea Cavalli

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 a decrease 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.

Andrea Cavalli is Professor of Medicinal Chemistry at the University of Bologna and Director of Computational and Chemical Biology at the Italian Institute of Technology, Genova, where he is also Deputy Director for the Research Domain ”Computational Sciences”.

Prof. Cavalli’s research has combined computational chemistry and physics with biology and drug discovery. He has developed and applied algorithms and protocols to accelerate and enhance the discovery of novel drug candidates in different therapeutic areas, including cancer, Alzheimer’s disease, and neglected tropical diseases. He has been a pioneer in the use of molecular dynamics simulations and related methods within drug discovery programs, and in 2014 he founded a high-tech startup company (BiKi Technologies), which develops tools based on statistical mechanics for drug discovery. In 2003, he was awarded the Farmindustria Prize for Pharmaceutical Research.

Modesto Orozco

HPC and BigData approaches in CoVid-19 research

At a time where we though major threats for human health would never come from infection diseases, CoVid-19 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. CoVid-19 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 CoVid-19 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 CoVid-19 research. I will comment a couple of initiatives done in Barcelona to: i) optimize the use of HPC resource to CoVid-19 research and ii) optimize the way in which the structural information on COVID19 system is stored, analyzed and shared.

Modesto Orozco is Director of the Integrative Biology Program at the Institute for Research in Biomedicine, Barcelona, and of the Joint IRB-BSC Research Program in Computational Biology.  

He acts as a consultant for scientific bodies in Spain and Europe, and is the Founder and President of Nostrum Biodiscovery a Biotech Company devoted to rational drug design. Prof. Orozco has been, or is advisor for several biotech and pharmaceutical companies, among others: Lab. Uriach, Lab. Almirall, Lab. Salvat, Pfizer Inc., Boehringer Ing., Kraft Pharm., Lab. Palau Pharma, Amgen Inc., and Nurix Inc. He is the recipient of an advanced ERC grant, and coordinator of H2020 projects in the domain of biosimulations.

His main interest is the understanding of biological systems from first principles. Topics of specific focus include the understanding of the mechanism of flexibility and signal transduction in proteins and the connection between physical properties of nucleic acids and their function, with special emphasis in the study of chromatin. He also contributes to the development of the Self-Consistent Reaction Field method to account for polarization, and is a developer of QM/MM approaches for large systems.

Q & A