Syntheses and neuraminidase inhibitory activity of multisubstituted cyclopentane amide derivatives

Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins

Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSE_train, Y-randomization test, MSE_test, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSE_train. For the test dataset, proposed QSAR model shows lower value of MSE_test compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC₅₀ values of neuraminidase inhibitors that have not been experimentally tested.

Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation of IAV-NA PDB entries of 9 subtypes is described, providing a list of residues constituting the protein-ligand binding sites. The results of induced-fit docking approach point out key residues of wild-type NA participating in hydrogen bonds and/or ionic interactions with peramivir, among which Arg 368 is responsible for a peramivir-NA ionic interaction. Mutations on this residue greatly reduced the binding affinity of peramivir with NA, with 3 mutations R378Q, R378K and R378L (NA6) capable of deteriorating the docking performance of peramivir by over 50%. 200 compounds from 6-scaffolds were docked into these 3 mutant versions, revealing 18 compounds giving the most promising results. Among them, CMC-2012-7-1527-56 (benzoic acid scaffold, IC50 = 32 nM in inhibitory assays with IAV) is deemed the most potential inhibitor of mutant NA resisting both peramivir and zanamivir, and should be further investigated.

A robust quantitative structure-activity relationship modelling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on the rank-bridge estimator

Linear regression model is frequently encountered in quantitative structure-activity relationship (QSAR) modelling. The traditional estimation of regression model parameters is based on the normal assumption of the response variable (biological activity) and therefore, it is sensitive to outliers or heavy-tailed distributions. Robust penalized regression methods have been given considerable attention because they combine the robust estimation method with penalty terms to perform QSAR parameter estimation and variable selection (descriptor selection) simultaneously. In this paper, based on bridge penalty, a robust QSAR model of the influenza neuraminidase a/PR/8/34 (H1N1) inhibitors is proposed as a resistant method to the existence of outliers or heavy-tailed errors. The basic idea is to combine the rank regression and the bridge penalty together to produce the rank-bridge method. The rank-bridge model is internally and externally validated based on Qint2 , QLGO2 , QBoot2 , MSEtrain , Y-randomization test, Qext2 , MSEtest and the applicability domain (AD). The validation results indicate that the rank-bridge model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the rank-bridge model for training dataset outperforms the other two used modelling methods. Rank-bridge model shows the highest Qint2 , QLGO2 and QBoot2 , and the lowest MSEtrain . For the test dataset, rank-bridge model shows higher external validation value ( Qext2 = 0.824), and lower value of MSEtest compared with the other methods, indicating its higher predictive ability.

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

Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine. The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.

A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine

Descriptor selection is a procedure widely used in chemometrics. The aim is to select the best subset of descriptors relevant to the quantitative structure-activity relationship (QSAR) study being considered. In this paper, a new descriptor selection method for the QSAR classification model is proposed by adding a new weight inside L1-norm. The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors.

Influenza Neuraminidase Inhibitors: Synthetic Approaches, Derivatives and Biological Activity

Despite being a common viral disease, influenza has very negative consequences, causing the death of around half a million people each year. A neuraminidase located on the surface of the virus plays an important role in viral reproduction by contributing to the release of viruses from infected host cells. The treatment of influenza is mainly based on the administration of neuraminidase inhibitors. The neuraminidase inhibitors zanamivir, laninamivir, oseltamivir and peramivir have been commercialized and have been demonstrated to be potent influenza viral neuraminidase inhibitors against most influenza strains. In order to create more potent neuraminidase inhibitors and fight against the surge in resistance resulting from naturally-occurring mutations, these anti-influenza drugs have been used as templates for the development of new neuraminidase inhibitors through structure-activity relationship studies. Here, we review the synthetic routes to these commercial drugs, the modifications which have been performed on these structures and the effects of these modifications on their inhibitory activity.

Asymmetric synthesis of cyclopentanes bearing four contiguous stereocenters via an NHC-catalyzed Michael/Michael/esterification domino reaction

An NHC-catalyzed Michael/Michael/esterification domino reaction via homoenolate/enolate intermediates for the asymmetric synthesis of tetrasubstituted cyclopentanes bearing four contiguous stereocenters is described. A variety of α,β-unsaturated aldehydes and 2-nitroallylic acetates react well with good domino yields and high stereoselectivities.

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models

Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 μM against H1N1 NA and 44.5-114.1 μM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

Synthesis of highly functionalized fluorinated cispentacin derivatives

Fluorinated highly functionalized cispentacin derivatives were synthetised starting from an unsaturated bicyclic β-lactam through C=C bond functionalization via the dipolar cycloaddition of a nitrile oxide, isoxazoline opening, and fluorination by OH/F exchange.

Recent advances in neuraminidase inhibitor development as anti-influenza drugs

The recent emergence of the highly pathogenic H5N1 subtype of avian influenza virus (AIV) and of the new type of human influenza A (H1N1) have emphasized the need for the development of effective anti-influenza drugs. Presently, neuraminidase (NA) inhibitors are widely used in the treatment and prophylaxis of human influenza virus infection, and tremendous efforts have been made to develop more potent NA inhibitors to combat resistance and new influenza viruses. In this review, we discuss the structural characteristics of NA catalytic domains and the recent developments of new NA inhibitors using structure-based drug design strategies. These drugs include analogues of zanamivir, analogues of oseltamivir, analogues of peramivir, and analogues of aromatic carboxylic acid and present promising options for therapeutics or leads for further development of NA inhibitors that may be useful in the event of a future influenza pandemic.

Synthesis of highly functionalized β-aminocyclopentanecarboxylate stereoisomers by reductive ring opening reaction of isoxazolines

A rapid and simple procedure was devised for the synthesis of multifunctionalized cyclic β-amino esters and γ-amino alcohols via the 1,3-dipolar cycloaddition of nitrile oxides to β-aminocyclopentenecarboxylates. The opening of the isoxazoline reductive ring to the corresponding highly functionalized 2-aminocyclopentanecarboxylates occurred stereoselectively with good yields.

Anti-influenza drugs: the development of sialidase inhibitors

Viruses, particularly those that are harmful to humans, are the 'silent terrorists' of the twenty-first century. Well over four million humans die per annum as a result of viral infections alone. The scourge of influenza virus has plagued mankind throughout the ages. The fact that new viral strains emerge on a regular basis, particularly out of Asia, establishes a continual socio-economic threat to mankind. The arrival of the highly pathogenic avian influenza H5N1 heightened the threat of a potential human pandemic to the point where many countries have put in place 'preparedness plans' to defend against such an outcome. The discovery of the first designer influenza virus sialidase inhibitor and anti-influenza drug Relenza, and subsequently Tamiflu, has now inspired a number of continuing efforts towards the discovery of next generation anti-influenza drugs. Such drugs may act as 'first-line-of-defence' against the spread of influenza infection and buy time for necessary vaccine development particularly in a human pandemic setting. Furthermore, the fact that influenza virus can develop resistance to therapeutics makes these continuing efforts extremely important. An overview of the role of the virus-associated glycoprotein sialidase (neuraminidase) and some of the most recent developments towards the discovery of anti-influenza drugs based on the inhibition of influenza virus sialidase is provided in this chapter.

Anti-influenza virus agents: synthesis and mode of action

Annual epidemics of influenza virus infection are responsible for considerable morbidity and mortality, and pandemics are much more devastating. Considerable knowledge of viral infectivity and replication has been acquired, but many details still have to be elucidated and the virus remains a challenging target for drug design and development. This review provides an overview of the antiviral drugs targeting the influenza viral replicative cycle. Included are a brief description of their chemical syntheses and biological activities. For other reviews, see References1-9.

Antiviral agents active against influenza A viruses

The recent outbreaks of avian influenza A (H5N1) virus, its expanding geographic distribution and its ability to transfer to humans and cause severe infection have raised serious concerns about the measures available to control an avian or human pandemic of influenza A. In anticipation of such a pandemic, several preventive and therapeutic strategies have been proposed, including the stockpiling of antiviral drugs, in particular the neuraminidase inhibitors oseltamivir (Tamiflu; Roche) and zanamivir (Relenza; GlaxoSmithKline). This article reviews agents that have been shown to have activity against influenza A viruses and discusses their therapeutic potential, and also describes emerging strategies for targeting these viruses.

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.

Neuraminidase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 22 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity, for discovering new potent neuraminidase (NA) inhibitors to fight against avian influenza virus. The best hypothesis (Hypo1), consisting of five features, namely, one positive ionizable group, one negative ionizable group, one hydrophobic point, and two hydrogen-bond donors, has a correlation coefficient of 0.902, a root mean square deviation of 1.392, and a cost difference of 72.88, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 88 known NA inhibitors in our test set with a correlation coefficient of 0.818 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.

A QSAR study on influenza neuraminidase inhibitors

Influenza is a major respiratory infection associated with significant morbidity in the general population and mortality in elderly and high-risk patients. It is an RNA virus that contains two major surface glycoproteins, neuraminidase and hemagglutinin. These proteins are essential for infection. Neuraminidase has been found to be a potential target to control influenza virus. In this paper, we have developed 17 quantitative structure-activity relationships (QSAR) for different sets of compounds to understand chemical-biological interactions governing their activities toward influenza neuraminidase.

Comparison of the anti-influenza virus activity of cyclopentane derivatives with oseltamivir and zanamivir in vivo

Cyclopentane derivatives, designated as BCX-1812, BCX-1827, BCX-1898, and BCX-1923, were tested in parallel with oseltamivir carboxylate and zanamivir for the in vivo activity in mice infected with A/Turkey/Mas/76 X A/Beijing/32/92 (H6N2) influenza virus. The compounds were tested orally and intranasally at different dose levels. BCX-1812, BCX-1827, and BCX-1923 showed more than 50% protection at 1mg/kg/day dose level on oral treatment. The intranasal treatment was 100% effective even at 0.01 mg/kg/day for all four compounds. On comparison with oseltamivir carboxylate and zanamivir, these four cyclopentane derivatives have shown equal or better efficacies. The synthesis of two new compounds, BCX-1898 and BCX-1923, is also described.

QSTR with extended topochemical atom indices. Part 5: Modeling of the acute toxicity of phenylsulfonyl carboxylates to Vibrio fischeri using genetic function approximation

In continuation of our recent efforts to model the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri using principal component factor analysis, the present paper deals with modeling of the same data set with extended topochemical atom (ETA) indices using genetic function approximation (GFA) as the statistical tool. The statistical quality of the best model using ETA descriptors is as follows: n=56, Q2=0.771, Ra2=0.861, R=0.935, F=69.0 (df5,50), s=0.172, AVRES=0.128. This equation was better in statistical quality than that obtained previously using principal component factor analysis as the data-preprocessing step. An attempt was also made to model the data set with different nonETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-State parameters apart from physicochemical parameters like AlogP98, MolRef and H_bond_acceptor) and the best model showed the following quality: n=56, Q2=0.805, Ra2=0.820, R=0.913, F=53.5 (df4,51), s=0.196, AVRES=0.136. An attempt to use both ETA and nonETA parameters lead to an equation which is marginally better than the best ETA model: n=56, Q2=0.779, Ra2=0.865, R=0.937, F=71.7 (df5,50), s=0.169, AVRES=0.127. Use of the ETA indices suggested negative contributions of steric bulk, functionality of C10, volume of substituents on C10 and functionalities of chloro and nitro substituents at X1 position and positive contributions of functionalities of C4 and C1 and presence of substituents with electronegative atoms, especially at R2 and R3 positions. Furthermore, absence of substituents at R2 and R3 positions decreases toxicity. This study corroborates the results obtained from principal component factor analysis and suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing significantly to the acute toxicity of phenylsulfonyl carboxylates to V. fischeri.