Influenza A H1N1 Virus 2009 Synthetic Hemagglutinin and Neuraminidase Peptides for Antibody Detection

Discovery of new quinoline derivatives bearing 1-aryl-1,2,3-triazole motif as influenza H1N1 virus neuraminidase inhibitors

Sporadically and periodically, influenza outbreaks threaten global health and the economy. Antigen drift-induced influenza virus mutations hamper antiviral drug development. Thus, a novel antiviral agent is urgently needed to address medication inefficacy issues. Herein, sixteen new quinoline-triazole hybrids 6a-h and 9a-h were prepared and evaluated in vitro against the H1N1 virus. In particular, 6d, 6e, and 9b showed promising H1N1 antiviral activity with selective index (SI) CC50/IC50 values of 15.8, 37, and 29.15. After that, the inhibition rates for various mechanisms of action (virus replication, adsorption, and virucidal activity) were investigated for the most efficient candidates 6d, 6e, and 9b. Additionally, their ability to inhibit neuraminidase was evaluated. With an IC50 value of 0.30 µM, hybrid 6d demonstrated effective and comparable inhibitory activity to Oseltamivir. Ultimately, molecular modeling investigations, encompassing molecular docking and molecular dynamic simulations, were conducted to provide a scientific basis for the observed antiviral results.

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

Elevated Levels of Cytotoxicity, Cytokines, and Anti-SARS-CoV-2 Antibodies in Mild Cases of COVID-19

Current evidence shows higher production of cytokines and antibodies against severe acute respiratory coronavirus 2 (SARS-CoV-2) in severe and critical cases of Coronavirus Disease 2019 (COVID-19) in comparison with patients with moderate or mild disease. A recent hypothesis proposes an important role of genotoxicity and cytotoxicity in the induction of the cytokine storm observed in some patients at later stages of the disease. Interestingly, in this study, we report significantly higher levels of interleukin (IL)-1β, IL-6, MCP-1, and IL-4 cytokines in mild COVID-19 patients versus severe cases, as well as a high frequency of karyorrhexis (median [Me] = 364 vs. 20 cells) and karyolysis (Me = 266 vs. 52 cells) in the mucosal epithelial cells of both groups of patients compared with uninfected individuals. Although we observed higher levels of anti-SARS-CoV-2 IgM and IgG antibodies in COVID-19 patients, IgM antibodies were significantly higher only in mild cases, for the N and the S viral antigens. High levels of IgG antibodies were observed in both mild and severe cases. Our results showed elevated concentrations of proinflammatory and anti-inflammatory cytokines in mild cases, which may reflect an active innate immune response and could be related to the higher IgM and IgG antibody levels found in those patients. In addition, we found that SARS-CoV-2 infection induces cytotoxic damage in the oral mucosa, highlighting the importance of studying the genotoxic and cytotoxic events induced by infection and its role in the pathophysiology of COVID-19.

Evaluation of a mimotope of the Rickettsia outer membrane protein A (OmpA) as an antigen in enzyme-linked immunosorbent assay to detect rickettsiosis in capybaras (Hydrochoerus hydrochaeris), horses (Equus caballus), and opossums (Didelphis sp.)

Rickettsia rickettsii is the etiological agent of Rocky Mountain spotted fever, which is an important tick-borne zoonosis and, in Brazil, it causes Brazilian spotted fever, which has high lethality rate. This study aimed to evaluate a synthetic peptide corresponding to a segment of the outer membrane protein A (OmpA) as an antigen in a serological test for the diagnosis of rickettsial infections. The amino acid sequence of the peptide was selected by predicting B cell epitopes using B Cell Epitope Prediction (Immune Epitope Database and Analysis Resource) and Epitopia and OmpA sequences of Rickettsia rickettsii strain 'Brazil' and Rickettsia parkeri strains 'Maculatum 20' and 'Portsmouth'. A peptide with amino acid sequence common to both Rickettsia species was synthesized and arbitrarily named OmpA-pLMC. To evaluate this peptide in enzyme-linked immunosorbent assay (ELISA), serum samples of capybara (Hydrochoerus hydrochaeris), horse (Equus caballus), and opossum (Didelphis albiventris) that had been previously tested by indirect immunofluorescence assay (IFA) for rickettsial infection were separated into IFA-positive and IFA-negative groups and used in the assay. There were no significant differences in ELISA optical density (OD) values between IFA-positive and IFA-negative groups with horse samples. The mean OD values were significantly higher in the IFA-positive capybara serum samples (IFA-pos vs. IFA-neg = 2.389 ± 0.761 vs. 1.760 ± 0.840). However, receiver operating characteristic (ROC) curve analysis did not show significant diagnostic parameters. On the other hand, 12 out of 14 (85.7%) opossum samples of the IFA-positive group showed reactivity in ELISA, and this was significantly higher than of the IFA-negative group (0.7196 ± 0.440 vs. 0.2318 ± 0.098, respectively; 85.7% sensitivity, 100% specificity). Therefore, our results show that OmpA-pLMC has a potential to be used in immunodiagnostic assays to detect spotted fever group rickettsial infections.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN (R train ​ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (R train ​ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.