Coronavirus Resistance Database (CoV-RDB): SARS-CoV-2 susceptibility to monoclonal antibodies, convalescent plasma, and plasma from vaccinated persons

  1. Philip L. Tzou
  2. Kaiming Tao
  3. Sergei L. Kosakovsky Pond
  4. Robert W. Shafer

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Abstract

As novel SARS-CoV-2 variants with different patterns of spike mutations have emerged, the susceptibility of these variants to neutralization by antibodies has been rapidly assessed. However, neutralization data are generated using different approaches and are scattered across different publications making it difficult for these data to be located and synthesized. The Stanford Coronavirus Resistance Database (CoV-RDB; https://covdb.stanford.edu ) is designed to house comprehensively curated published data on the neutralizing susceptibility of SARS-CoV-2 variants and spike mutations to monoclonal antibodies (mAbs), convalescent plasma (CP), and vaccinee plasma (VP). As of October 2021, CoV-RDB contains 186 publications including 64 (34%) containing 7,328 neutralizing mAb susceptibility results, 96 (52%) containing 11,390 neutralizing CP susceptibility results, and 125 (68%) containing 20,872 neutralizing VP results. The database also records which spike mutations are selected during in vitro passage of SARS-CoV-2 in the presence of mAbs and which emerge in persons receiving mAbs as treatment. The CoV-RDB interface interactively displays neutralizing susceptibility data at different levels of granularity by filtering and/or aggregating query results according to one or more experimental conditions. The CoV-RDB website provides a companion sequence analysis program that outputs information about mutations present in a submitted sequence and that also assists users in determining the appropriate mutation-detection thresholds for identifying non-consensus amino acids. The most recent data underlying the CoV-RDB can be downloaded in its entirety from a Github repository in a documented machine-readable format.

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  1. ScreenIT

    SciScore for 10.1101/2021.11.24.469823: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    The CoV-RDB data model is made up of four major entities: published references, viral mutations and variants, antibodies (including mAbs, CP, and VP), and experimental results.
    VP
    suggested: None
    Software and Algorithms
    SentencesResources
    Curation of published references: Published references included in the CoV-RDB are obtained weekly from three literature sources:i) PubMed using the search term “SARS-CoV-2”; ii) BioRxiv/MedRxiv COVID-19 SARS-CoV-2 preprint servers; and iii) the Research Square SARS-CoV-2/COVID-19 preprint server.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study limitations: Neutralizing susceptibility data are highly heterogeneous often resulting in discordant results across studies [16, 17]. There are three main sources for this heterogeneity. First, the composition of neutralizing antibodies among previously infected and vaccinated individuals is heterogeneous [6, 18]. Second, there are different types of neutralizing assays including those performed in cell culture using pseudotyped viruses, chimeric viruses, recombinant viruses, and clinical isolates and, more recently, surrogate neutralizing assays that assess the ability of antibodies to block the interaction between the SARS-CoV-2 RBD and ACE2. Third, results for the same sample against a virus variant can differ even among laboratories using the same type of assay as a result of differences in virus inoculum size, the cells used for culture, and viral replication endpoints [16,17,19]. As neutralizing assays become more standardized and as external controls such as those provided by the WHO [20] are increasingly used, it is likely that reproducibility across studies will improve. Data on the clinical significance of SARS-CoV-2 neutralizing susceptibility is continually evolving [11,21–26]. The utility of neutralizing antibodies as a correlate of immune protection is ultimately determined in epidemiological studies. Therefore, a database devoted to protective immunity should ideally contain both laboratory and epidemiological data. The main obstacle to expanding CoV-RDB ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.11.24.469823v1 on bioRxiv
  3. Version published to 10.1101/2021.11.24.469823v2 on bioRxiv