Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.

Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.

Assessment of the Abbott BinaxNOW SARS-CoV-2 rapid antigen test against viral variants of concern.

As the emergence of SARS-CoV-2 variants brings the global pandemic to new levels, the performance of current rapid antigen tests against variants of concern and interest (VOC/I) is of significant public health concern. Here, we report assessment of the Abbot BinaxNOW COVID-19 Antigen Self-Test. Using genetically sequenced remnant clinical samples collected from individuals positive for SARS-CoV-2, we assessed the performance of BinaxNOW against the variants that currently pose public health threats. We measured the limit of detection of BinaxNOW against various VOC/I in a blinded manner. BinaxNOW successfully detected the Omicron (B.1.1.529), Mu (B.1.621), Delta (B.1.617.2), Lambda (C.37), Gamma (P.1), Alpha (B.1.1.7), Beta (B.1.351), Eta (B.1.525), and P.2 variants and at low viral concentrations. BinaxNOW also detected the Omicron variant in individual remnant clinical samples. Overall, these data indicate that this inexpensive and simple-to-use, FDA-authorized and broadly distributed rapid test can reliably detect Omicron, Delta, and other VOC/I.

Comparison of anti-SARS-CoV-2 activity and intracellular metabolism of remdesivir and its parent nucleoside.

Remdesivir, a monophosphate prodrug of nucleoside analog GS-441524, is widely used for the treatment of moderate to severe COVID-19. It has been suggested to use GS-441524 instead of remdesivir in the clinic and in new inhalation formulations. Thus, we compared the anti-SARS-CoV-2 activity of remdesivir and GS-441524 in Vero E6, Vero CCL-81, Calu-3, Caco-2 â€‹cells, and anti-HCoV-OC43 activity in Huh-7 â€‹cells. We also compared the cellular pharmacology of these two compounds in Vero E6, Vero CCL-81, Calu-3, Caco-2, Huh-7, 293T, BHK-21, 3T3 and human airway epithelial (HAE) cells. Overall, remdesivir exhibited greater potency and superior intracellular metabolism than GS-441524 except in Vero E6 and Vero CCL-81 â€‹cells.