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1.
Microb Risk Anal ; : 100198, 2021 Dec 04.
Article in English | MEDLINE | ID: covidwho-1549995

ABSTRACT

Thermodynamic equilibrium models predict the infectivity of novel and emerging viruses using molecular data including the binding affinity of the virus to the host cell (as represented by the association constant Ka_virus_T) and the probability, pvirogenesis, of the virus replicating after entry to the cell. Here those models are adapted based on the principles of ligand binding to macromolecules to assess the effect on virus infectivity of inhibitor molecules which target specific proteins of the virus. Three types of inhibitor are considered using the thermodynamic equilibrium model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the human lung with parameters for the strength and nature of the interaction between the target virus protein and the inhibitor molecule. The first is competitive inhibition of the SARS-CoV-2 spike glycoprotein (SGP) trimer binding to its human angiotensin converting enzyme 2 (ACE2) receptor by unfractionated heparin (UFH). Using a novel approach presented here, a value of Ka_virus_T = 3.53 × 1017 M-1 is calculated for SARS-CoV-2 from the IC50 for inhibition by UFH of SARS-CoV-2 plaque formation in cell culture together with the dissociation constant KVI of 0.73 × 10-10 M reported for heparin binding to SARS-CoV-2 SGP trimer. Such a high Ka_virus_T limits the effectiveness of competitive inhibitors such as UFH. The second is the attachment of a nanoparticle such as a zinc oxide tetrapod (ZnOT) to the virus shell as for herpes simplex virus (HSV). The increase in molecular weight through ZnOT attachment is predicted to decrease Ka_virus_T by orders of magnitude by making the entropy change (ΔSa_immob) on immobilisation of the ZnOT:virus complex on cell binding more negative than for the virus alone. According to the model, ZnOT acts synergistically with UFH at the IC50 of 33 µg/cm3 which together decrease viral infectivity by 61,000-fold compared to the two-fold and three-fold decreases predicted for UFH alone at the IC50 and for ZnOT alone respectively. According to the model here, UFH alone at its peak deliverable dose to the lung of 1,000 µg/cm3 only decreases infectivity by 31-fold. Practicable approaches to target and decrease ΔSa_immob for respiratory viruses should therefore be considered. The combination of decreasing ΔSa_immob together with blocking the interaction of virus surface protein with its host cell receptor may achieve synergistic effects for faecal-oral viruses and HSV. The third is reversible noncompetitive inhibition of the viral main protease (Mpro) for which the decrease in pvirogenesis is assumed to be proportional to the decrease in enzyme activity as predicted by enzyme kinetic equations for a given concentration of inhibitor which binds to Mpro with dissociation constant Ki. Virologists reporting viral inhibition studies are urged to report the concentration of cells in the cell culture experiment as this is a key parameter in estimating Ka_virus_T here.

2.
Microbial Risk Analysis ; : 100175, 2021.
Article in English | ScienceDirect | ID: covidwho-1267338

ABSTRACT

Risk ranking tools to prioritise the impact of exotic animal diseases in a country or area are useful to assist risk managers in optimising the allocation of available resources for the prevention and control of infectious diseases. Although several such tools have already been developed, few focus on the probability of entry of an exotic pathogen into a territory and even fewer are able to rank multiple pathogens at the same time. We developed a semi-quantitative multi-criteria model to estimate the probability of incursion of an exotic pathogen into a European country and use Italy as a case study. We consider the import of 37 animal diseases of importance to Italy, based on OIE notification guidelines, and determine a disease status around the world based on current country-level reporting to the OIE. We identify seven possible pathways for the introduction of a pathogen and for each of them we determine a scoring system to assess for each disease the probability of introduction via each pathway. These scores, alongside the disease status, are used to calculate an overall risk score for each pathogen. The results indicate that the risk of incursion of Echinococcus multilocularis, African swine fever virus, Trichinella sp., lumpy skin disease and foot and mouth disease virus are ranked the highest. Additional analyses identified that the disease ranking is sensitive to the relative importance of the pathways of entry and also the impact of potential mitigation measures. The model is designed to be periodically updated with new data as they become available, e.g. global disease prevalence and trade volume. Therefore, it can be used by official authorities on a regular basis to obtain up-to-date results and consequentially strengthen surveillance towards those pathogens with the highest probability of entry.

3.
Microb Risk Anal ; 16: 100140, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-779468

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) infect the human respiratory tract. A prototype thermodynamic equilibrium model is presented here for the probability of the virions getting through the mucus barrier and infecting epithelial cells based on the binding affinity (Kmucin) of the virions to mucin molecules in the mucus and parameters for binding and infection of the epithelial cell. Both MERS-CoV and SARS-CoV-2 bind strongly to their cellular receptors, DDP4 and ACE2, respectively, and infect very efficiently both bronchus and lung ex vivo cell cultures which are not protected by a mucus barrier. According to the model, mucin binding could reduce the infectivity for MERS-CoV compared to SARS-CoV-2 by at least 100-fold depending on the magnitude of Kmucin. Specifically Kmucin values up to 106 M-1 have little protective effect and thus the mucus barrier would not remove SARS-CoV-2 which does not bind to sialic acids (SA) and hence would have a very low Kmucin. Depending on the viability of individual virions, the ID50 for SARS-CoV-2 is estimated to be ~500 virions (viral RNA genomic copies) representing 1 to 2 pfu. In contrast MERS-CoV binds both SA and human mucin and a Kmucin of 5 × 109 M-1 as reported for lectins would mop up 99.83% of the virus according to the model with the ID50 for MERS-CoV estimated to be ~295,000 virions (viral RNA genomic copies) representing 819 pfu. This could in part explain why MERS-CoV is poorly transmitted from human to human compared to SARS-CoV-2. Some coronaviruses use an esterase to escape the mucin, although MERS-CoV does not. Instead, it is shown here that "clustering" of virions into single aerosol particles as recently reported for rotavirus in extracellular vesicles could provide a co-operative mechanism whereby MERS-CoV could theoretically overcome the mucin barrier locally and a small proportion of 10 µm diameter aerosol particles could contain ~70 virions based on reported maximum levels in saliva. Although recent evidence suggests SARS-CoV-2 initiates infection in the nasal epithelium, the thermodynamic equilibrium models presented here could complement published approaches for modelling the physical entry of pathogens to the lung based on the fate and transport of the pathogen particles (as for anthrax spores) to develop a dose-response model for aerosol exposure to respiratory viruses. This would enable the infectivity through aerosols to be defined based on molecular parameters as well as physical parameters. The role of the spike proteins of MERS-CoV and SARS-CoV-2 binding to SA and heparan sulphate, respectively, may be to aid non-specific attachment to the host cell. It is proposed that a high Kmucin is the cost for subsequent binding of MERS-CoV to SAs on the cell surface to partially overcome the unfavourable entropy of immobilisation as the virus adopts the correct orientation for spike protein interactions with its protein cellular receptor DPP4.

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