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Comparison of a prototype SARS-CoV-2 lateral flow immunoassay with the BinaxNOW™ COVID-19 Antigen CARD (preprint)
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.09.16.22279736
ABSTRACT
Background Robust diagnostics, capable of detecting multiple variant of SARS-CoV-2 are necessary to mitigate the COVID-19 pandemic. In this study we directly compare the diagnostic capabilities of an LFI engineered with monoclonal antibodies (mAbs) originating from SARS-CoV-2 NP immunizations to the Abbott BinaxNOW COVID-19 Antigen CARD. Methods Here we established a library of 18 mAbs specific to SARS-CoV-2 NP and used two of these mAbs (1CV7 and 1CV14) to generate a prototype antigen-detection lateral flow immunoassay (LFI). Samples consisting of remnant RT-PCR positive patient nasopharyngeal swabs preserved in viral transport media (VTM) were tested on the 1CV7/1CV14 LFI and the commercially available BinaxNOW test. Assays were allowed to resolve and results were recorded by two observers. Findings A total of 98 remnant SARS-CoV-2 positive patient specimens were tested on both the 1CV7/1CV14 LFI and the BinaxNOW test. The 1CV7/1CV14 LFI detected 71 of the total 98 specimens, while the BinaxNOW test detected 52 of the 98 specimens. Additionally, the 1CV7/1CV14 LFI consistently detected samples with higher RT-PCR cycle threshold values than the BinaxNOW test. Interpretation The 1CV7/1CV14 LFI outperformed the BinaxNOW test in the detection of BA.2, BA.2.12.1, and BA.5 Omicron sub-variants when testing remnant RT-PCR positive patient nasopharyngeal swabs diluted in viral transport media. BA.1 and BA.4 detection was comparable. The data suggest that mAbs derived from SARS-CoV-2 NP can aid in a more sensitive diagnostic immunoassay for COVID-19.
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Full text: Available Collection: Preprints Database: medRxiv Main subject: Virus Diseases / COVID-19 Language: English Year: 2022 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: Virus Diseases / COVID-19 Language: English Year: 2022 Document Type: Preprint