A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine

Two classes of anti-influenza drugs are currently available for the treatment or prophylaxis of influenza. These are the adamantanes (amantadine and rimantadine), which block the activity of the M2 ion channel of influenza A viruses (but not influenza B viruses), and the neuraminidase inhibitors (NAIs), which act by binding to the enzymatic site of the influenza neuraminidase (NA) thereby preventing progeny virions from being released from the host cell during viral replication. Antiviral resistance can occur in influenza viruses and render the drug ineffective for the treatment of patients. Virtually all influenza A viruses currently circulating in the human population are resistant to the adamantanes, while in comparison these viruses remain susceptible to the NAIs. In particular, very low NAI resistance has been observed in pandemic A(H1N1) 2009 viruses, even though unprecedented amounts of these drugs were used.

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Influenza A viruses of swine circulating in the United States during 2009–2014 are susceptible to neuraminidase inhibitors but show lineage-dependent resistance to adamantanes

Antiviral Research ◽

10.1016/j.antiviral.2015.02.004 ◽

2015 ◽

Vol 117 ◽

pp. 10-19 ◽

Cited By ~ 11

Author(s):

Tatiana Baranovich ◽

Justin Bahl ◽

Bindumadhav M. Marathe ◽

Marie Culhane ◽

Evelyn Stigger-Rosser ◽

...

Keyword(s):

United States ◽

Influenza A ◽

The United States ◽

Neuraminidase Inhibitors ◽

Influenza A Viruses

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Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

10.1101/2021.04.17.440161 ◽

2021 ◽

Author(s):

Taryn M. Lucas ◽

Chitrak Gupta ◽

Meghan O. Altman ◽

Emi Sanchez ◽

Matthew R. Naticchia ◽

...

Keyword(s):

Pattern Recognition ◽

Mammalian Cells ◽

Influenza A ◽

Learning Algorithm ◽

Sialic Acids ◽

Soluble Form ◽

Support Vector ◽

Glycan Array ◽

Influenza A Viruses ◽

New Host

ABSTRACTInfluenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa to gain entry and initiate infection. Rapid detection of changes in IAV specificity towards host glycan classes can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HA) to bind sialic acids linked to larger glycan structures and a switch in HA specificity from α2,3-to α2,6-linked sialoglycans is considered a prerequisite for viral transmission from birds to humans. While the changes in HA structure associated with the evolution of binding phenotype have been mapped, the influence of glycan receptor presentation on IAV specificity remains obscured. Here, we describe a glycan array platform which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx, including glycan type and valency of the glycoconjugates and their surface density, impact IAV binding. We found that H1N1 virus produced in embryonated chicken eggs, which recognizes both receptor types, exclusively engaged mucin-mimetics carrying α2,3-linked sialic acids in their soluble form. The virus was able utilize both receptor structures when the probes were immobilized on the array; however, increasing density in the mucin-mimetic brush diminished viral adhesion. Propagation in mammalian cells produced a change in receptor pattern recognition by the virus, without altering its HA affinity, toward improved binding of α2,6-sialylated mucin mimetics and reduced sensitivity to surface crowding of the probes. Application of a support vector machine (SVM) learning algorithm as part of the glycan array binding analysis efficiently characterized this shift in binding preference and may prove useful to study the evolution of viral responses to a new host.

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Treatment of Highly Pathogenic H7N9 Virus-Infected Mice with Baloxavir Marboxil

Viruses ◽

10.3390/v11111066 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1066 ◽

Cited By ~ 1

Author(s):

Maki Kiso ◽

Seiya Yamayoshi ◽

Yuri Furusawa ◽

Masaki Imai ◽

Yoshihiro Kawaoka

Keyword(s):

Virus Infection ◽

Influenza A ◽

H7n9 Virus ◽

Neuraminidase Inhibitors ◽

Influenza A Viruses ◽

Lethal Challenge ◽

Highly Pathogenic ◽

Limited Efficacy ◽

H7n9 Influenza ◽

Influenza H7n9 Virus

Viral neuraminidase inhibitors show limited efficacy in mice infected with H7N9 influenza A viruses isolated from humans. Although baloxavir marboxil protected mice from lethal challenge infection with a low pathogenic avian influenza H7N9 virus isolated from a human, its efficacy in mice infected with a recent highly pathogenic version of H7N9 human isolates is unknown. Here, we examined the efficacy of baloxavir marboxil in mice infected with a highly pathogenic human H7N9 virus, A/Guangdong/17SF003/2016. Treatment of infected mice with a single 1.5 mg/kg dose of baloxavir marboxil protected mice from the highly pathogenic human H7N9 virus infection as effectively as oseltamivir treatment at 50 mg/kg twice a day for five days. Daily treatment for five days at 15 or 50 mg/kg of baloxavir marboxil showed superior therapeutic efficacy, largely preventing virus replication in respiratory organs. These results indicate that baloxavir marboxil is a valuable candidate treatment for human patients suffering from highly pathogenic H7N9 virus infection.

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Quantum chemical and molecular dynamics studies of imidazoline derivatives as corrosion inhibitor and quantitative structure–activity relationship (QSAR) analysis using the support vector machine (SVM) method

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633614500126 ◽

2014 ◽

Vol 13 (02) ◽

pp. 1450012 ◽

Cited By ~ 3

Author(s):

Lei Du ◽

Hongxia Zhao ◽

Haixiang Hu ◽

Xiuhui Zhang ◽

Lin Ji ◽

...

Keyword(s):

Molecular Dynamics ◽

Support Vector Machine ◽

Quantum Chemical ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Activity Relationship ◽

Support Vector ◽

Quantitative Structure ◽

Hydrophilic Group ◽

Structure Activity

The inhibition performance of 10 imidazoline molecules with number of carbon from 15 to 21 of hydrocarbon straight-chain was studied by weight-loss method and theoretical approaches. The main purpose was to build a quantitative structure–activity relationship (QSAR) between the structural properties and the inhibition efficiencies, and then to predict efficiencies of new corrosion inhibitors. The quantum chemical calculation suggested that the active region of imidazoline molecules was located on the imidazoline ring and hydrophilic group, and active sites were concentrated on the nitrogen atoms of the molecules and carbon atoms of hydrophilic group. A model in accordance with the real experimental solution was built in the molecular dynamics, and the equilibrium configuration indicated that the imidazoline molecules were adsorbed on Fe (110) surface in parallel manner. Descriptors for QSAR model building were selected by principal component analysis (PCA) and the model was built by the support vector machine (SVM) approach, which shows good performance since the value of correlation coefficient (R) was 0.99 and the root mean square error (RMSE) was 0.94. Additionally, six new imidazoline molecules were theoretically designed and the inhibition efficiencies of three molecules were predicted to be more than 86% by the established QSAR model.

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