Advances in COVID-19 Computational Research

Webinar | 16 March 2021 | 13:00

16-03-2021
PHARMAINF

The webinar "Advances in COVID-19 Computational Research" is organized by the Pharma Informatics Unit, Athena RC, and will take place on Tuesday, March 16th, 2021 (12:55 pm).

In the first session, Faidon Brotzakis will talk about a strategy based on metainference cryo-electron microscopy towards the targeting of the trimeric transmembrane glycoprotein that the virus uses to recognise and bind its host cells. In the second session, Ioanna Tzoulaki will present an analysis of the diagnostic models for detecting COVID-19 and the prognostic models for predicting mortality risk and severe outcomes published to date. In the last session of the webinar Panos Macheras will focus on the reaction between susceptible and infected subjects under not well-mixed conditions in order to get insights on the COVID-19 spreading in absence/presence of preventive measures.

To participate in the event, please register here.

 

Program

12:55 -13:00   Introduction:    Prof. Ioannis Emiris, ATHENA Research Center
 
13:00 -13:45   Session 1:    "Determination of Intermediate State Structures in the Opening Pathway of SARS-
CoV-2 Spike Using Cryo-Electron Microscopy", Faidon Brotzakis, FEBS post-doctoral
fellow, Centre of Misfolding Diseases, Chemistry Department, University of Cambridge, UK
13:40 -13:45    Q & A
 
13:45 -14: 25   Session 2:   "Prediction models for diagnosis and prognosis of covid-19 infection",
Ioanna Tzoulaki,  Department of Hygiene and Epidemiology, School of Medicine, 
University of Ioannina, Greece
14:25 -14:30    Q & A
 
14:30 -15:10    Session 3:    "Modeling the worldwide COVID-19 spreading", Panos Macheras, PharmaInformatics
  Unit, ATHENA Research Center, Athens, Greece
15:10 -15:15    Q & A
 
15:15 -15:20    Closing:         Prof. Ioannis Emiris, ATHENA Research Center
 

Abstracts

 
 
       
     
 
     FEBS post-doctoral fellow, Centre of Misfolding Diseases, Chemistry Department,
     University of Cambridge, UK    
 
Determination of Intermediate State Structures in the Opening Pathway of SARS-CoV-2 Spike Using Cryo-Electron Microscopy
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID19, a highly infectious disease that is severely affecting our society and welfare systems. In   order to develop therapeutic interventions against this condition, one promising strategy is to target spike, the trimeric transmembrane glycoprotein that the virus uses to recognise and bind its host cells. Here we use a metainference cryo-electron microscopy approach to determine the opening pathway that brings spike from its inactive (closed) conformation to its active (open) one. The knowledge of the structures of the intermediate states of spike along these opening pathways enables us to identify a cryptic pocket that is not exposed in the open and closed states. We then identify compounds that bind the cryptic pocket by screening a library of repurposed drugs. These results underline the opportunities offered by the determination of the structures of the intermediate states populated during the dynamics of proteins to allow the therapeutic targeting of otherwise invisible cryptic binding sites.
 
 
     
 
 
      Ioanna Tzoulaki 
      Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Greece 
 
 
Prediction models for diagnosis and prognosis of covid-19 infection
More than 91 diagnostic models for detecting covid-19 and 50 prognostic models for predicting mortality risk and severe outcomes have been published to date. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. However, all models were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and vague reporting. Similar risk factors have been identified in another study on risk factors for positive and negative COVID19 tests in the UK biobank population. 
 
References
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Damen JAA, Debray TPA, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Kreuzberger N, Lohman A, Luijken K, Ma J, Andaur CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Tzoulaki I, Takada T, van Kuijk SMJ, van Royen FS, Wallisch C, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 2020; 369. doi: https://doi.org/10.1136/bmj.m1328
Chadeau-Hyam M, Bodinier B, Elliott J, Whitaker MD, Tzoulaki I, Vermeulen R, Kelly-Irving M, Delpierre C, Elliott P. Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data. Int J Epidemiol. 2020 Oct 1;49(5):1454-1467. 

 
 
 
     
     PharmaInformatics Unit, Research Center ATHENA, Athens, Greece
 
 
Modeling the worldwide COVID-19 spreading
The reaction between susceptible and infected subjects has been studied under the well-mixed hypothesis for almost a century. Here, a consistent analysis for a not well-mixed system using fractal kinetics principles is presented. COVID-19 data were analyzed to get insights on the disease spreading in absence/presence of preventive measures. A three-parameter model was derived. The “fractal” exponent h of time larger than unity can capture the impact of preventive measures affecting population mobility. The h=1 case, which is a power of time model, accurately describes the situation without such measures in line with a herd immunity policy. The pandemic spread in four model countries (France, Greece, Italy and Spain) for the first 10 months has gone through 4 stages: stages 1 and 3 with limited to no measures, stages 2 and 4 with varying lockdown conditions. For each stage and country two or three model parameters have been determined using appropriate fitting procedures. The fractal kinetics model was found to be more akin to real life. Model predictions and their implications lead to the conclusion that the fractal kinetics model can be used as a prototype for the analysis of all contagious airborne pandemics.
 
References
K. Kosmidis, P. Macheras .A fractal kinetics SI model can explain the dynamics of COVID-19 epidemics.   (2020) . PLoS ONE 15(8): e0237304. https://doi.org/10.1371/journal.pone.0237304
P.  Macheras, K. Kosmidis, P. Chryssafidis.  Demystifying the spreading of pandemics I: The fractal kinetics SI model quantifies the dynamics of COVID-19 , medRxiv 2020.11.15.20232132; doi: https://doi.org/10.1101/2020.11.15.20232132
P.  Macheras, A. Tsekouras, P. Chryssafidis. Fractal kinetics: The common denominator in drug release kinetics and COVID-19 spreading. Submitted.

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