2-((1H-indol-3-yl)thio)-N-phenyl-acetamides: SARS-CoV-2 RNA-dependent RNA polymerase inhibitors

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of Coronavirus Disease 2019 (COVID-19) pandemic. Despite intensive and global efforts to discover and develop novel antiviral therapies, only Remdesivir has been approved as a treatment for COVID-19. Therefore, effective antiviral therapeutics are still urgently needed to combat and halt the pandemic. Viral RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 demonstrates high potential as a reliable target for the development of antivirals. We previously developed a cell-based assay to assess the efficiency of compounds that target SARS-CoV-2 RdRp, as well as their tolerance to viral exoribonuclease-mediated proof-reading. In our previous study, we discovered that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides specifically targets the RdRp of both respiratory syncytial virus (RSV) and influenza A virus. Thus, we hypothesize that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides may also have the ability to inhibit SARS-CoV-2 replication by targeting its RdRp activity. In this research, we test a compound library containing 103 of 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides against SARS-CoV-2 RdRp, using our cell-based assay. Among these compounds, the top five candidates strongly inhibit SARS-CoV-2 RdRp activity while exhibiting low cytotoxicity and resistance to viral exoribonuclease. Compound 6-72-2a is the most promising candidate with the lowest EC50 value of 1.41 mu M and highest selectivity index (CC50/EC50) (above 70.92). Furthermore, our data suggests that 4-46b and 6-72-2a also inhibit the replication of HCoV-OC43 and HCoV-NL63 virus in a dose-dependent manner. Compounds 4-46b and 6-72-2a exhibit EC50 values of 1.13 mu M and 0.94 mu M, respectively, on HCoV-OC43 viral replication. However, higher concentrations of these compounds are needed to effectively block HCoV-NL63 replication. Together, our findings successfully identified 4-46b and 6-72-2a as promising inhibitors against SARS-CoV-2 RdRp.

Forecasting Unemployment Using Internet Search Data via PRISM

Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users’ Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method Penalized Regression with Inferred Seasonality Module (PRISM), which uses publicly available online search data from Google. PRISM is a semiparametric method, motivated by a general state-space formulation, and employs nonparametric seasonal decomposition and penalized regression. For forecasting unemployment initial claims, PRISM outperforms all previously available methods, including forecasting during the 2008–2009 financial crisis period and near-future forecasting during the COVID-19 pandemic period, when unemployment initial claims both rose rapidly. The timely and accurate unemployment forecasts by PRISM could aid government agencies and financial institutions to assess the economic trend and make well-informed decisions, especially in the face of economic turbulence. © 2021 American Statistical Association.