Broad-spectrum antiviral activity of two structurally analogous CYP3A inhibitors against pathogenic human coronaviruses in vitro.

Coronaviruses pose a permanent risk of outbreaks, with three highly pathogenic species and strains (SARS-CoV, MERS-CoV, SARS-CoV-2) having emerged in the last twenty years. Limited antiviral therapies are currently available and their efficacy in randomized clinical trials enrolling SARS-CoV-2 patients has not been consistent, highlighting the need for more potent treatments. We previously showed that cobicistat, a clinically approved inhibitor of Cytochrome P450-3A (CYP3A), has direct antiviral activity against early circulating SARS-CoV-2 strains in vitro and in Syrian hamsters. Cobicistat is a derivative of ritonavir, which is co-administered as pharmacoenhancer with the SARS-CoV-2 protease inhibitor nirmatrelvir, to inhibit its metabolization by CPY3A and preserve its antiviral efficacy. Here, we used automated image analysis for a screening and parallel comparison of the anti-coronavirus effects of cobicistat and ritonavir. Our data show that both drugs display antiviral activity at low micromolar concentrations against multiple SARS-CoV-2 variants in vitro, including epidemiologically relevant Omicron subvariants. Despite their close structural similarity, we found that cobicistat is more potent than ritonavir, as shown by significantly lower EC50 values in monotherapy and higher levels of viral suppression when used in combination with nirmatrelvir. Finally, we show that the antiviral activity of both cobicistat and ritonavir is maintained against other human coronaviruses, including HCoV-229E and the highly pathogenic MERS-CoV. Overall, our results demonstrate that cobicistat has more potent anti-coronavirus activity than ritonavir and suggest that dose adjustments could pave the way to the use of both drugs as broad-spectrum antivirals against highly pathogenic human coronaviruses.

Real-time estimation of immunological responses against emerging SARS-CoV-2 variants in the UK: a mathematical modelling study.

BACKGROUND: The emergence of SARS-CoV-2 variants and COVID-19 vaccination have resulted in complex exposure histories. Rapid assessment of the effects of these exposures on neutralising antibodies against SARS-CoV-2 infection is crucial for informing vaccine strategy and epidemic management. We aimed to investigate heterogeneity in individual-level and population-level antibody kinetics to emerging variants by previous SARS-CoV-2 exposure history, to examine implications for real-time estimation, and to examine the effects of vaccine-campaign timing. METHODS: Our Bayesian hierarchical model of antibody kinetics estimated neutralising-antibody trajectories against a panel of SARS-CoV-2 variants quantified with a live virus microneutralisation assay and informed by individual-level COVID-19 vaccination and SARS-CoV-2 infection histories. Antibody titre trajectories were modelled with a piecewise linear function that depended on the key biological quantities of an initial titre value, time the peak titre is reached, set-point time, and corresponding rates of increase and decrease for gradients between two timing parameters. All process parameters were estimated at both the individual level and the population level. We analysed data from participants in the University College London Hospitals-Francis Crick Institute Legacy study cohort (NCT04750356) who underwent surveillance for SARS-CoV-2 either through asymptomatic mandatory occupational health screening once per week between April 1, 2020, and May 31, 2022, or symptom-based testing between April 1, 2020, and Feb 1, 2023. People included in the Legacy study were either Crick employees or health-care workers at three London hospitals, older than 18 years, and gave written informed consent. Legacy excluded people who were unable or unwilling to give informed consent and those not employed by a qualifying institution. We segmented data to include vaccination events occurring up to 150 days before the emergence of three variants of concern: delta, BA.2, and XBB 1.5. We split the data for each wave into two categories: real-time and retrospective. The real-time dataset contained neutralising-antibody titres collected up to the date of emergence in each wave; the retrospective dataset contained all samples until the next SARS-CoV-2 exposure of each individual, whether vaccination or infection. FINDINGS: We included data from 335 participants in the delta wave analysis, 223 (67%) of whom were female and 112 (33%) of whom were male (median age 40 years, IQR 22-58); data from 385 participants in the BA.2 wave analysis, 271 (70%) of whom were female and 114 (30%) of whom were male (41 years, 22-60); and data from 248 participants in the XBB 1.5 wave analysis, 191 (77%) of whom were female, 56 (23%) of whom were male, and one (<1%) of whom preferred not to say (40 years, 21-59). Overall, we included 968 exposures (vaccinations) across 1895 serum samples in the model. For the delta wave, we estimated peak titre values as 490·0 IC50 (95% credible interval 224·3-1515·9) for people with no previous infection and as 702·4 IC50 (300·8-2322·7) for people with a previous infection before omicron; the delta wave did not include people with a previous omicron infection. For the BA.2 wave, we estimated peak titre values as 858·1 IC50 (689·8-1363·2) for people with no previous infection, 1020·7 IC50 (725·9-1722·6) for people with a previous infection before omicron, and 1422·0 IC50 (679·2-3027·3) for people with a previous omicron infection. For the XBB 1.5 wave, we estimated peak titre values as 703·2 IC50 (415·0-3197·8) for people with no previous infection, 1215·9 IC50 (511·6-7338·7) for people with a previous infection before omicron, and 1556·3 IC50 (757·2-7907·9) for people with a previous omicron infection. INTERPRETATION: Our study shows the feasibility of real-time estimation of antibody kinetics before SARS-CoV-2 variant emergence. This estimation is valuable for understanding how specific combinations of SARS-CoV-2 exposures influence antibody kinetics and for examining how COVID-19 vaccination-campaign timing could affect population-level immunity to emerging variants. FUNDING: Wellcome Trust, National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK Research and Innovation, UK Medical Research Council, Francis Crick Institute, and Genotype-to-Phenotype National Virology Consortium.

Differences in airborne stability of SARS-CoV-2 variants of concern is impacted by alkalinity of surrogates of respiratory aerosol.

The mechanistic factors hypothesized to be key drivers for the loss of infectivity of viruses in the aerosol phase often remain speculative. Using a next-generation bioaerosol technology, we report measurements of the aero-stability of several SARS-CoV-2 variants of concern in aerosol droplets of well-defined size and composition at high (90%) and low (40%) relative humidity (RH) upwards of 40 min. When compared with the ancestral virus, the infectivity of the Delta variant displayed different decay profiles. At low RH, a loss of viral infectivity of approximately 55% was observed over the initial 5 s for both variants. Regardless of RH and variant, greater than 95% of the viral infectivity was lost after 40 min of being aerosolized. Aero-stability of the variants correlate with their sensitivities to alkaline pH. Removal of all acidic vapours dramatically increased the rate of infectivity decay, with 90% loss after 2 min, while the addition of nitric acid vapour improved aero-stability. Similar aero-stability in droplets of artificial saliva and growth medium was observed. A model to predict loss of viral infectivity is proposed: at high RH, the high pH of exhaled aerosol drives viral infectivity loss; at low RH, high salt content limits the loss of viral infectivity.

The P323L substitution in the SARS-CoV-2 polymerase (NSP12) confers a selective advantage during infection.

BACKGROUND: The mutational landscape of SARS-CoV-2 varies at the dominant viral genome sequence and minor genomic variant population. During the COVID-19 pandemic, an early substitution in the genome was the D614G change in the spike protein, associated with an increase in transmissibility. Genomes with D614G are accompanied by a P323L substitution in the viral polymerase (NSP12). However, P323L is not thought to be under strong selective pressure. RESULTS: Investigation of P323L/D614G substitutions in the population shows rapid emergence during the containment phase and early surge phase during the first wave. These substitutions emerge from minor genomic variants which become dominant viral genome sequence. This is investigated in vivo and in vitro using SARS-CoV-2 with P323 and D614 in the dominant genome sequence and L323 and G614 in the minor variant population. During infection, there is rapid selection of L323 into the dominant viral genome sequence but not G614. Reverse genetics is used to create two viruses (either P323 or L323) with the same genetic background. L323 shows greater abundance of viral RNA and proteins and a smaller plaque morphology than P323. CONCLUSIONS: These data suggest that P323L is an important contribution in the emergence of variants with transmission advantages. Sequence analysis of viral populations suggests it may be possible to predict the emergence of a new variant based on tracking the frequency of minor variant genomes. The ability to predict an emerging variant of SARS-CoV-2 in the global landscape may aid in the evaluation of medical countermeasures and non-pharmaceutical interventions.