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1. Virtual Screening of the Indonesian Medicinal Plant and Zinc Databases for Potential Inhibitors of the RNA-Dependent RNA Polymerase (RdRp) of 2019 Novel Coronavirus

   https://doi.org/10.22146/ijc.56120  

   Arba, Muhammad ; Nur-Hidayat, Andry ; Usman, Ida ; Yanuar, Arry ; Wahyudi, Setyanto Tri ; Fleischer, Gilbert ; Brunt, Dylan James ; Wu, Chun ( October 2020 , Indonesian Journal of Chemistry)

   null (Ed.)

   The novel coronavirus disease 19 (Covid-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a pandemic across the world, which necessitate the need for the antiviral drug discovery. One of the potential protein targets for coronavirus treatment is RNA-dependent RNA polymerase. It is the key enzyme in the viral replication machinery, and it does not exist in human beings, therefore its targeting has been considered as a strategic approach. Here we describe the identification of potential hits from Indonesian Herbal and ZINC databases. The pharmacophore modeling was employed followed by molecular docking and dynamics simulation for 40 ns. 151 and 14480 hit molecules were retrieved from Indonesian herbal and ZINC databases, respectively. Three hits that were selected based on the structural analysis were stable during 40 ns, while binding energy prediction further implied that ZINC1529045114, ZINC169730811, and 9-Ribosyl-trans-zeatin had tighter binding affinities compared to Remdesivir. The ZINC169730811 had the strongest affinity toward RdRp compared to the other two hits including Remdesivir and its binding was corroborated by electrostatic, van der Waals, and nonpolar contribution for solvation energies. The present study offers three hits showing tighter binding to RdRp based on MM-PBSA binding energy prediction for further experimental verification. 

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2. Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?

   https://doi.org/10.3390/pharmaceutics13081181  

   Conway, Jessica M. ; Abel zur Wiesch, Pia ( August 2021 , Pharmaceutics)

   The antiviral remdesivir has been approved by regulatory bodies such as the European Medicines Agency (EMA) and the US Food and Drug administration (FDA) for the treatment of COVID-19. However, its efficacy is debated and toxicity concerns might limit the therapeutic range of this drug. Computational models that aid in balancing efficacy and toxicity would be of great help. Parametrizing models is difficult because the prodrug remdesivir is metabolized to its active form (RDV-TP) upon cell entry, which complicates dose–activity relationships. Here, we employ a computational model that allows drug efficacy predictions based on the binding affinity of RDV-TP for its target polymerase in SARS-CoV-2. We identify an optimal infusion rate to maximize remdesivir efficacy. We also assess drug efficacy in suppressing both wild-type and resistant strains, and thereby describe a drug regimen that may select for resistance. Our results differ from predictions using prodrug dose–response curves (pseudo-EC50s). We expect that reaching 90% inhibition (EC90) is insufficient to suppress SARS-CoV-2 in the lungs. While standard dosing mildly inhibits viral polymerase and therefore likely reduces morbidity, we also expect selection for resistant mutants for most realistic parameter ranges. To increase efficacy and safeguard against resistance, we recommend more clinical trials with dosing regimens that substantially increase the levels of RDV-TP and/or pair remdesivir with companion antivirals. 

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3. Probing remdesivir nucleotide analogue insertion to SARS-CoV-2 RNA dependent RNA polymerase in viral replication

   https://doi.org/10.1039/D1ME00088H  

   Romero, Moises Ernesto ; Long, Chunhong ; La Rocco, Daniel ; Keerthi, Anusha Mysore ; Xu, Dajun ; Yu, Jin ( November 2021 , Molecular Systems Design & Engineering)

   Remdesivir (RDV) prodrug can be metabolized into a triphosphate form nucleotide analogue (RDV-TP) to bind and insert into the active site of viral RNA dependent RNA polymerase (RdRp) to further interfere with viral genome replication. In this work, we computationally studied how RDV-TP binds and inserts to the SARS-CoV-2 RdRp active site, in comparison with natural nucleotide substrate adenosine triphosphate (ATP). To do that, we first constructed atomic structural models of an initial binding complex (active site open) and a substrate insertion complex (active site closed), based on high-resolution cryo-EM structures determined recently for SARS-CoV-2 RdRp or non-structural protein (nsp) 12, in complex with accessory protein factors nsp7 and nsp8. By conducting all-atom molecular dynamics simulation with umbrella sampling strategies on the nucleotide insertion between the open and closed state RdRp complexes, our studies show that RDV-TP can initially bind in a comparatively stabilized state to the viral RdRp active site, as it primarily forms base stacking with the template uracil nucleotide (nt +1), which under freely fluctuations supports a low free energy barrier of the RDV-TP insertion (∼1.5 kcal mol −1 ). In comparison, the corresponding natural substrate ATP binds initially to the RdRp active site in Watson–Crick base pairing with the template nt, and inserts into the active site with a medium low free energy barrier (∼2.6 kcal mol −1 ), when the fluctuations of the template nt are well quenched. The simulations also show that the initial base stacking of RDV-TP with the template can be specifically stabilized by motif C-S759, S682 (near motif B) with the base, and motif G-K500 with the template backbone. Although the RDV-TP insertion can be hindered by motif F-R555/R553 interaction with the triphosphate, the ATP insertion seems to be facilitated by such interactions. The inserted RDV-TP and ATP can be further distinguished by specific sugar interaction with motif B-T687 and motif A-D623, respectively. 

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4. Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2

   https://doi.org/10.3390/v14030605  

   Ferrari Gianlupi, Juliano ; Mapder, Tarunendu ; Sego, T. J. ; Sluka, James P. ; Quinney, Sara K. ; Craig, Morgan ; Stratford, Robert E. ; Glazier, James A. ( March 2022 , Viruses)

   We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism. 

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5. Severe Acute Respiratory Syndrome Coronavirus 2 Delta Vaccine Breakthrough Transmissibility in Alachua County, Florida

   https://doi.org/10.1093/cid/ciac197  

   Rife Magalis, Brittany ; Rich, Shannan ; Tagliamonte, Massimiliano S. ; Mavian, Carla ; Cash, Melanie N. ; Riva, Alberto ; Marini, Simone ; Amador, David Moraga ; Zhang, Yanping ; Shapiro, Jerne ; et al ( March 2022 , Clinical Infectious Diseases)

   Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant has caused a dramatic resurgence in infections in the United Sates, raising questions regarding potential transmissibility among vaccinated individuals.

   Between October 2020 and July 2021, we sequenced 4439 SARS-CoV-2 full genomes, 23% of all known infections in Alachua County, Florida, including 109 vaccine breakthrough cases. Univariate and multivariate regression analyses were conducted to evaluate associations between viral RNA burden and patient characteristics. Contact tracing and phylogenetic analysis were used to investigate direct transmissions involving vaccinated individuals.

   The majority of breakthrough sequences with lineage assignment were classified as Delta variants (74.6%) and occurred, on average, about 3 months (104 ± 57.5 days) after full vaccination, at the same time (June-July 2021) of Delta variant exponential spread within the county. Six Delta variant transmission pairs between fully vaccinated individuals were identified through contact tracing, 3 of which were confirmed by phylogenetic analysis. Delta breakthroughs exhibited broad viral RNA copy number values during acute infection (interquartile range, 1.2-8.64 Log copies/mL), on average 38% lower than matched unvaccinated patients (3.29-10.81 Log copies/mL, P < .00001). Nevertheless, 49% to 50% of all breakthroughs, and 56% to 60% of Delta-infected breakthroughs exhibited viral RNA levels above the transmissibility threshold (4 Log copies/mL) irrespective of time after vaccination.

   Delta infection transmissibility and general viral RNA quantification patterns in vaccinated individuals suggest limited levels of sterilizing immunity that need to be considered by public health policies. In particular, ongoing evaluation of vaccine boosters should specifically address whether extra vaccine doses curb breakthrough contribution to epidemic spread.

    

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