ABSTRACT
PURPOSE: Many effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed, but a weaker response in individuals undergoing anticancer treatment has been reported. This study evaluates the immunogenic status and safety of SARS-CoV-2 vaccines for patients with non-small-cell lung cancer (NSCLC), receiving tegafur-uracil (UFT) as postoperative adjuvant chemotherapy. METHODS: The subjects of this prospective study were 40 patients who underwent surgery for NSCLC and received SARS-CoV-2 vaccines postoperatively. We compared the antibody titers of SARS-CoV-2 vaccines and the adverse events between patients who received adjuvant UFT and patients who did not. RESULTS: The mean anti-S1 IgG titers were not significantly different between the UFT and without-UFT groups (mean optimal density, 0.194 vs. 0.205; P = 0.76). Multivariate analysis identified the period after the second vaccination as an independent predictor of anti-S1 IgG titer (P = 0.049), but not the UFT status (with or without-UFT treatment; P = 0.47). The prevalence of adverse events did not differ significantly between the groups, and no severe adverse events occurred. CONCLUSIONS: The efficacy and safety of the SARS-CoV-2 vaccines for NSCLC patients who received postoperative adjuvant UFT chemotherapy were comparable to those for NSCLC patients who did not receive postoperative adjuvant UFT chemotherapy. CLINICAL TRIAL REGISTRATION: This study was registered with the University Hospital Medical Information Network (UMIN) in Japan (UMIN000047380).
ABSTRACT
Objectives: Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2. Methods: We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity. Results: The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity. Conclusion: We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.