RESUMO
The novel betacoranavirus SARS-CoV-2 causes a form of severe pneumonia disease, termed COVID-19 (coronavirus disease 2019). Recombinant human antibodies are proven potent neutralizers of viruses and can block the interaction of viral surface proteins with their host receptors. To develop neutralizing anti-SARS-CoV-2 antibodies, antibody gene libraries from convalescent COVID-19 patients were constructed and recombinant antibody fragments (scFv) against the receptor binding domain (RBD) of the S1 subunit of the viral spike (S) protein were selected by phage display. The selected antibodies were produced in the scFv-Fc format and 30 showed more than 80% inhibition of spike (S1-S2) binding to cells expressing ACE2, assessed by flow cytometry screening assay. The majority of these inhibiting antibodies are derived from the VH3-66 V-gene. The antibody STE90-C11 showed a sub nM IC50 in a plaque-based live SARS-CoV-2 neutralization assay. The in vivo efficacy of the antibody was demonstrated in the Syrian hamster and in the hACE2 mice model using a silenced human IgG1 Fc part. The crystal structure of STE90-C11 Fab in complex with SARS-CoV-2-RBD was solved at 2.0 [A] resolution showing that the antibody binds at the same region as ACE2 to RBD. The binding and inhibtion of STE90-C11 is not blocked by many known RBD mutations including N439K, L452R, E484K or L452R+E484Q (emerging B.1.617). STE90-C11 derived human IgG1 with Fc{gamma}R silenced Fc (COR-101) is currently undergoing Phase Ib/II clinical trials for the treatment of moderate to severe COVID-19. In BriefHuman antibodies were selected from convalescent COVID-19 patients using antibody phage display. The antibody STE90-C11 is neutralizing authentic SARS-CoV-2 virus in vitro and in vivo and the crystal structure of STE90-C11 in complex with SARS-CoV-2-RBD revealed that this antibody is binding in the RBD-ACE2 interface. S1 binding of STE90-C11 and inhibition of ACE2 binding is not blocked by many known RBD mutations.
RESUMO
The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user's standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user's perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.