Research progress in novel coronavirus (2019-nCoV)-related drugs in vitro and in vivo

In December 2019 in Wuhan City (Hubei Province, China), multiple cases of patients with pneumonia infected by a new type of coronavirus were noted. With the spread of the epidemic, other cases in China and overseas have also been found. On 12 January 2020, the World Health Organization tentatively named it "2019 Novel Coronavirus" (2019-nCoV). This is a new type of virus, which is highly infectious and can cause severe respiratory diseases. A clinically efficacious treatment is lacking. We reviewed the guidelines for recommended therapeutic drugs and drug-development advances with the aim of providing a reference for clinical treatment of 2019-nCoV infection.

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2 (preprint)

Background The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social. The ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health and economic stability worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19.Methods We first established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus S protein and its host receptor ACE2. Two compound libraries of 2,864 molecules were screened with this platform. Selected candidate compounds were validated by SARS-CoV-2_S pseudotyped lentivirus and ACE2-overexpressing cell system. Molecular docking was used to analyze the interaction between S-protein and compounds.Results We identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S-protein and the human cell ACE2 receptor. Additionally, tannic acid showed moderate inhibitory effect on the interaction of S-protein and ACE2.Conclusion This platform is a rapid, sensitive, specific, and high throughput system, and available for screening large compound libraries. TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug.

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2 (preprint)

ABSTRACT The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social and economic stability. In this study, we established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus S protein and its host receptor ACE2. This platform is a rapid, sensitive, specific, and high throughput system. With this platform, we screened two compound libraries of 2,864 molecules and identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S-protein and the human cell ACE2 receptor. This data suggests that TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug. SIGNIFICANCE The ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19. In this present study, we screened two compound libraries of 2,864 molecules and identified a potent inhibitor (TS-984) for blocking the coronavirus S-protein and the human cell ACE2 receptor. TS-984 might have the potential to be developed into an effective anti-coronavirus drug for treating COVID-19.

Analysis of the WHO ICTRP for novel coronavirus clinical trial registrations.

Up-to-date information on the current progress made in the research and development to control the global COVID-19 pandemic is important. The study aimed to analyze the clinical trial characteristics and vaccine development progress of the new Coronavirus Disease 2019 (COVID-19) registered with the World Health Organization International Clinical Trial Registry Platform (WHO ICTRP).A comprehensive search of COVID-19 clinical trials since the establishment of the ICTRP to June 11, 2020, was conducted to record and analyze relevant characteristics. Chi-Squared test was used to compare the statistical differences between different research types, interventions, and sources.A total of 3282 COVID-19 clinical trials in 17 clinical trial registration centers were registered with the WHO ICTRP. The main research sources for the present study were ClinicalTrials.gov and ChiCTR. There were significant differences in the parameters of study location (P = .000), number of participants (P = .000), study duration (P = .001), research stage (P = .000), randomization procedure (P = .000), and blinding method (P = .000) between the 2 registration sources. There were significant differences in all the parameters between different kinds of intervention methods. Hydroxychloroquine, plasma therapy, and Xiyanping injection were the high-frequency research drugs used. Ten different vaccine studies were registered under phases I-II.Amongst the studies researched, heterogeneity existed for various parameters. Differences in the type of study, interventions, and registration sources of the studies led to significant differences in certain parameters of the COVID-19 clinical trials. The statistics of high-frequency drugs and the progress of vaccine trials may provide an informative reference for the prevention and control of COVID-19.

Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race (preprint)

The novel coronavirus disease (COVID-19) pandemic is a global threat presenting health, economic and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health and shaping policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. Whats more, variation of intra-county environments creates spatial heterogeneity of transmission in different sub-regions. To address this issue, we develop a new human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behavior. This new modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and human mobility, business foot-traffic, race & ethnicity, and age-group are then investigated. The results reveal that in a college town (Dane County) the most important heterogeneity is spatial, while in a large city area (Milwaukee County) ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate on various reopening policies, which suggests that policymakers may need to take these heterogeneities into account very carefully when designing policies for mitigating the spread of COVID-19 and reopening.

Impacts of COVID-19 Lockdowns and Stimulus Payments on Low-income Population's Spending in the United States (preprint)

The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population’s spending along the inter-temporal, geo-spatial, and cross-category dimensions. Leveraging difference-in-differences and spatial association analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes followed by a higher rebound and a net gain after the stimulus payments; Southwest exhibited the highest initial decline whereas Southeast largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as cigar and liquor. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs to protect and promote the well-being of the low-income population amid the public health and economic crises.

Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic (preprint)

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analyzing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behavior changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States (preprint)

BackgroundThe novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. MethodsWe conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5 and O3 and county-level COVID-19 case-fatality and mortality rates in the US. We used both single and multipollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level healthcare capacity, phase-of-epidemic, population mobility, sociodemographic, socioeconomic status, behavior risk factors, and meteorological factors. Results1,027,799 COVID-19 cases and 58,489 deaths were reported in 3,122 US counties from January 22, 2020 to April 29, 2020, with an overall observed case-fatality rate of 5.8%. Spatial variations were observed for both COVID-19 death outcomes and long-term ambient air pollutant levels. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models (p-values<0.05). Per inter-quartile range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 7.1% (95% CI 1.2% to 13.4%) and 11.2% (95% CI 3.4% to 19.5%), respectively. We did not observe significant associations between long-term exposures to PM2.5 or O3 and COVID-19 death outcomes (p-values>0.05), although per IQR increase in PM2.5 (3.4 ug/m3) was marginally associated with 10.8% (95% CI: -1.1% to 24.1%) increase in COVID-19 mortality rate. Discussions and ConclusionsLong-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of longterm PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Moreover, continuation of current efforts to lower traffic emissions and ambient air pollution levels may be an important component of reducing population-level risk of COVID-19 deaths.

Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic (preprint)

The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and travel distance decay rate and dwell time at home change rate was -0.586 (95% CI: -0.742 ~ -0.370) and 0.526 (95% CI: 0.293 ~ 0.700), respectively. Increases in state-specific doubling time of total cases ranged from 1.04 ~ 6.86 days to 3.66 ~ 30.29 days after social distancing orders were put in place, consistent with mechanistic epidemic prediction models. Social distancing mandates reduce the spread of COVID-19 when they are followed.

Mapping county-level mobility pattern changes in the United States in response to COVID-19 (preprint)

To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed with the support of the National Science Foundation (NSF). It integrates geographic information systems (GIS) and daily updated human mobility statistical patterns derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support governmental decision-making, and help enhance community responses to the COVID-19 outbreak.

Mitigating COVID-19 outbreak via high testing capacity and strong transmission-intervention in the United States (preprint)

Most models of the COVID-19 pandemic in the United States do not consider geographic variation, and their relevance to public policies is not straightforward. We developed a mathematical model that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.