Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Massively Parallel Sequencing of Peritoneal and Splenic B Cell Repertoires Highlights Unique Properties of B-1 Cell Antibodies.

B-1 cells are a unique subset of B cells that are positively selected for expressing autoreactive BCRs. We isolated RNA from peritoneal (B-1a, B-1b, B-2) and splenic (B-1a, marginal zone, follicular) B cells from C57BL/6 mice and used 5'-RACE to amplify the IgH V region using massively parallel sequencing. By analyzing 379,000 functional transcripts, we demonstrate that B-1a cells use a distinct and restricted repertoire. All B-1 cell subsets, especially peritoneal B-1a cells, had a high proportion of sequences without N additions, suggesting predominantly prenatal development. Their transcripts differed markedly and uniquely contained VH11 and VH12 genes, which were rearranged only with a restricted selection of D and J genes, unlike other V genes. Compared to peritoneal B-1a, the peritoneal B-1b repertoire was larger, had little overlap with B-1a, and most sequences contained N additions. Similarly, the splenic B-1a repertoire differed from peritoneal B-1a sequences, having more unique sequences and more frequent N additions, suggesting influx of B-1a cells into the spleen from nonperitoneal sites. Two CDR3s, previously described as Abs to bromelain-treated RBCs, comprised 43% of peritoneal B-1a sequences. We show that a single-chain variable fragment designed after the most prevalent B-1a sequence bound oxidation-specific epitopes such as the phosphocholine of oxidized phospholipids. In summary, we provide the IgH V region library of six murine B cell subsets, including, to our knowledge for the first time, a comparison between B-1a and B-1b cells, and we highlight qualities of B-1 cell Abs that indicate unique selection processes.

Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina.

BACKGROUND: The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. METHODOLOGY/PRINCIPAL FINDINGS: Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. CONCLUSIONS/SIGNIFICANCE: In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.