QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release

Wide-Antimicrobial Spectrum of Picolinium Salts

Nosocomial infections, which greatly increase morbidity among hospitalized patients, together with growing antibiotic resistance still encourage many researchers to search for novel antimicrobial compounds. Picolinium salts with different lengths of alkyl chains (C12, C14, C16) were prepared by Menshutkin-like reaction and evaluated with respect to their biological activity, i.e., lipophilicity and critical micellar concentration. Picolinium salts with C14 and C16 side chains achieved similar or even better results when in terms of antimicrobial efficacy than benzalkoniums; notably, their fungicidal efficiency was substantially more potent. The position of the methyl substituent on the aromatic ring does not seem to affect antimicrobial activity, in contrast to the effect of length of the N-alkyl chain. Concurrently, picolinium salts exhibited satisfactory low cytotoxicity against mammalian cells, i.e., lower than that of benzalkonium compounds, which are considered as safe.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

Nicotinic receptor antagonists as treatments for nicotine abuse

Despite the proven efficacy of current pharmacotherapies for tobacco dependence, relapse rates continue to be high, indicating that novel medications are needed. Currently, several smoking cessation agents are available, including varenicline (Chantix®), bupropion (Zyban®), and cytisine (Tabex®). Varenicline and cytisine are partial agonists at the α4β2* nicotinic acetylcholine receptor (nAChR). Bupropion is an antidepressant but is also an antagonist at α3β2* ganglionic nAChRs. The rewarding effects of nicotine are mediated, in part, by nicotine-evoked dopamine (DA) release leading to sensitization, which is associated with repeated nicotine administration and nicotine addiction. Receptor antagonists that selectivity target central nAChR subtypes mediating nicotine-evoked DA release should have efficacy as tobacco use cessation agents with the therapeutic advantage of a limited side-effect profile. While α-conotoxin MII (α-CtxMII)-insensitive nAChRs (e.g., α4β2*) contribute to nicotine-evoked DA release, these nAChRs are widely distributed in the brain, and inhibition of these receptors may lead to nonselective and untoward effects. In contrast, α-CtxMII-sensitive nAChRs mediating nicotine-evoked DA release offer an advantage as targets for smoking cessation, due to their more restricted localization primarily to dopaminergic neurons. Small drug-like molecules that are selective antagonists at α-CtxMII-sensitive nAChR subtypes that contain α6 and β2 subunits have now been identified. Early research identified a variety of quaternary ammonium analogs that were potent and selective antagonists at nAChRs mediating nicotine-evoked DA release. More recent data have shown that novel, nonquaternary bis-1,2,5,6-tetrahydropyridine analogs potently inhibit (IC50<1nM) nicotine-evoked DA release in vitro by acting as antagonists at α-CtxMII-sensitive nAChR subtypes; these compounds also decrease NIC self-administration in rats.

Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC₅₀ values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r(2)=0.67, rmsd=0.87, q(2)=0.65 and loormsd=0.90; the statistical results for the ligand-based MLR model were: r(2)=0.89, rmsd=0.51, q(2)=0.85 and loormsd=0.58; the statistical results for the ligand-based NN model were the best: r(2)=0.95, rmsd=0.33, q(2)=0.90 and loormsd=0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer's disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.

Improving the inhibitory activity of arylidenaminoguanidine compounds at the N-methyl-D-aspartate receptor complex from a recursive computational-experimental structure-activity relationship study

Using a combination of both the partial least squares (PLS) and back-propagation artificial neural network (ANN) pattern recognition methods, several models have been developed to predict the activity of a series of arylidenaminoguanidine analogs as inhibitory modulators of the N-methyl-D-aspartate receptor complex. This was done by correlating structural and physicochemical descriptors obtained from computation software with the experimentally observed [(3)H]MK-801 displacement ability of a small library of synthesized and in vitro screened arylidenaminoguanidines. Results for the generated PLS model were r(2)=0.814, rmsd=0.208, rCV(2)=0.714, loormsd=0.261. The ANN model was created utilizing the eleven descriptors from the PLS model for comparison. The quality of the ANN model (r(2)=0.828, rmsd=0.200, rCV(2)=0.721, loormsd=0.257) is similar to the PLS model, and indicates that the feature between the inputs and the output is majorly linear. These computational models were able to predict inhibition of the NMDA receptor complex by this series of compounds in silico, affording a predictive structure-based 'pre-screening' paradigm for the arylideneaminoguanidine analogs.

A simple QSPR model for the prediction of the adsorbability of organic compounds onto activated carbon cloth

A quantitative structure-property relationship (QSPR) model was proposed between the molecular descriptors representing the molecular structure and the Freundlich adsorbability parameter (K) for a set of 55 organic compounds onto activated carbon cloth. The best linear model was composed of three descriptors, which were selected by stepwise multiple linear regression (MLR) analysis. The statistical parameters provided by the linear model were r² = 0.7744, r²(adj) = 0.7551, s = 0.169 for the training set; and r² = 0.6725, r²(adj) = 0.6316, s = 0.196 for the external test set, respectively. The stability and predictive power of the proposed model were further verified using Y-randomization tests, five-fold cross-validation and leave-many-out cross-validation. The model may give some insight into the main structural features that affect the adsorption of the investigated compounds onto activated carbon cloth.

Prediction of gas-phase reduced ion mobility constants (K0) from three-dimensional molecular structure representation

Quantitative structure–property relationship (QSPR) studies were performed for the prediction of gas-phase reduced ion mobility constants (K0) of diverse compounds based on three-dimensional (3D) molecular structure representation. The entire set of 159 compounds was divided into a training set of 120 compounds and a test set of 39 compounds according to Kennard and Stones algorithm. Multiple linear regression (MLR) analysis was employed to select the best subset of descriptors and to build linear models, whereas nonlinear models were developed by means of an artificial neural network (ANN). The obtained models with five descriptors involved show good predictive power for the test set: a squared correlation coefficient (R2) of 0.9029 and a standard error of estimation (s) of 0.0549 were achieved by the MLR model, whereas by the ANN model, R2 and s were 0.9292 and 0.496, respectively. The results of this study compare favorably to previously reported prediction methods for the ion mobility constants. In addition, the descriptors used in the models are discussed with respect to the structural features governing the mobility of the compounds.

QSPR studies of impact sensitivity of nitro energetic compounds using three-dimensional descriptors

The quantitative structure-property relationship (QSPR) studies were performed between molecular structures and impact sensitivity for a diverse set of nitro energetic compounds based on three-dimensional (3D) descriptors. The entire set of 156 compounds was divided into a training set of 127 compounds and a test set of 29 compounds according to Kennard and Stones algorithm. Multiple linear regression (MLR) analysis was employed to select the best subset of descriptors and to build linear models; while nonlinear models were developed by means of artificial neural network (ANN). The obtained models with ten descriptors involved show good predictive power for the test set: a squared correlation coefficient (R²) of 0.7222 and a standard error of estimation (s) of 0.177 were achieved by the MLR model; while by the ANN model, R² and s were 0.8658 and 0.130, respectively. Therefore, the proposed models can be used to predict the impact sensitivity of new nitro compounds for engineering.

Design, synthesis and interaction at the vesicular monoamine transporter-2 of lobeline analogs: potential pharmacotherapies for the treatment of psychostimulant abuse

The vesicular monoamine transporter-2 (VMAT2) is considered as a new target for the development of novel therapeutics to treat psychostimulant abuse. Current information on the structure, function and role of VMAT2 in psychostimulant abuse are presented. Lobeline, the major alkaloidal constituent of Lobelia inflata, interacts with nicotinic receptors and with VMAT2. Numerous studies have shown that lobeline inhibits both the neurochemical and behavioral effects of amphetamine in rodents, and behavioral studies demonstrate that lobeline has potential as a pharmacotherapy for psychostimulant abuse. Systematic structural modification of the lobeline molecule is described with the aim of improving selectivity and affinity for VMAT2 over neuronal nicotinic acetylcholine receptors and other neurotransmitter transporters. This has led to the discovery of more potent and selective ligands for VMAT2. In addition, a computational neural network analysis of the affinity of these lobeline analogs for VMAT2 has been carried out, which provides computational models that have predictive value in the rational design of VMAT2 ligands and is also useful in identifying drug candidates from virtual libraries for subsequent synthesis and evaluation.

QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima (lambda(max)) of organic dyes for dye-sensitized solar cells (DSSCs). The entire set of 70 dyes was divided into a training set of 53 dyes and a test set of 17 dyes according to Kennard and Stones algorithm. Three-dimensional (3D) descriptors were calculated to represent the dye molecules. A ten-descriptor model, with a squared correlation coefficient (R(2)) of 0.9543 and a standard error of estimation (s) of 14.7 nm, was produced by using the stepwise multilinear regression analysis (MLRA) on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-one-out cross-validation procedure, randomization tests, and validation through the external test set. All descriptors involved in the model were derived solely from the chemical structure of the dye molecules, which makes the model very useful to estimate the lambda(max) of dyes before they are actually synthesized.

Synthesis and antimicrobial activity of a series of optically active quaternary ammonium salts derived from phenylalanine

We synthesized nine quaternary ammonium compounds (QUATs) starting from phenylalanine, N-alkyl-N,N-dimethyl-(1-hydroxy-3-phenylpropyl)-2-ammonium bromides, which were prepared as optically pure substances. Five compounds were prepared as S-enantiomers and four compounds as R-enantiomers. These compounds were evaluated by their activities against bacteria and fungi. Three microbial strains were used in the study: the gram-negative bacteria Escherichia coli, the gram-positive bacteria Staphylococcus aureus and the fungi Candida albicans. The activities were expressed as minimum bactericidal or fungicidal concentrations (MBC). The most active compounds were (2S)-N-tetradecyl-N,N-dimethyl-(1-hydroxy-3-phenylpropyl)-2-ammonium bromide and (2R)-N-tetradecyl-N,N-dimethyl-(1-hydroxy-3-phenylpropyl)-2-ammonium bromide, with MBC values exceeding those of commercial benzalkoniumbromide (BAB) used as standard. The relationships between structure and biological activity of the tested QUATs were quantified by the bilinear model (QSAR) and are discussed.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds

OBJECTIVE

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals.

METHODS

Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software.

RESULTS

For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers.

SIGNIFICANCE

These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.

First-Principles Determination of Molecular Conformations of Indolizidine (-)-235B' in Solution

Indolizidine (-)-235B' is a particularly interesting natural product, as it is the currently known, most potent and subtype-selective open-channel blocker of the alpha4beta2 nicotinic acetylcholine receptor (nAChR). In the current study, extensive first-principles electronic structure calculations have been carried out in order to determine the stable molecular conformations and their relative free energies of the protonated and deprotonated states of (-)-235B' in the gas phase, in chloroform, and in aqueous solution. The (1)H and (13)C NMR chemical shifts calculated using the computationally determined dominant molecular conformation of the deprotonated state are all consistent with available experimental NMR spectra of (-)-235B' in chloroform, which suggests that the computationally determined molecular conformations are reasonable. Our computational results reveal for the first time that two geminal H atoms on carbon-3 (C3) of (-)-235B' have remarkably different chemical shifts (i.e. 3.24 and 2.03 ppm). The computational results help one to better understand and analyze the experimental (1)H NMR spectra of (-)-235B'. The finding of remarkably different chemical shifts of two C3 geminal H atoms in a certain molecular conformation of (-)-235B' may also be valuable in analysis of NMR spectra of other related ring-containing compounds. In addition, the pK(a) of (-)-235B' in aqueous solution is predicted to be ~9.7. All of the computational results provide a solid basis for future studies of the microscopic and phenomenological binding of various receptor proteins with the protonated and deprotonated structures of this unique open-channel blocker of alpha4beta2 nAChRs. This computational study also demonstrates how one can appropriately use computational modeling and spectroscopic analysis to address the structural and spectroscopic problems that cannot be addressed by experiments alone.

QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(-)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Maximal inhibition (I(max)) of the agonist effect is an important pharmacological property of inhibitors that interact with multiple receptor subtypes that are activated by the same agonist and which elicit the same functional response. This report represents the first QSAR study on a set of 66 mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating nicotine-evoked dopamine release, conducted using multi-linear regression (MLR) and neural network (NN) analysis with the maximal inhibition (I(max)) values of the antagonists as target values. The statistical results for the generated MLR model were: r(2)=0.89, rmsd=9.01, q(2)=0.83 and loormsd=11.1; the statistical results for the generated NN model were: r(2)=0.89, rmsd=8.98, q(2)=0.83 and loormsd=11.2. The maximal inhibition values of the compounds exhibited a good correlation with the predictions made by the QSAR models developed, which provide a basis for rationalizing selection of compounds for synthesis in the discovery of effective and selective second generation inhibitors of nAChRs mediating nicotine-evoked dopamine release.

Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks

A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid-water distribution coefficients (K(d)) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK(a), molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated K(d) data, were incorporated as a training set within a developed artificial neural network to predict K(d) for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined K(d) are presented which showed excellent correlation (R(2) > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K(d) for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K(d) values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.

Computational neural network analysis of the affinity of N-n-alkylnicotinium salts for the alpha4beta2* nicotinic acetylcholine receptor

Based on an 85 molecule database, linear regression with different size datasets and an artificial neural network approach have been used to build mathematical relationships to fit experimentally obtained affinity values (K(i)) of a series of mono- and bis-quaternary ammonium salts from [(3)H]nicotine binding assays using rat striatal membrane preparations. The fitted results were then used to analyze the pattern among the experimental K(i) values of a set of N-n-alkylnicotinium analogs with increasing n-alkyl chain length from 1 to 20 carbons. The affinity of these N-n-alkylnicotinium compounds was shown to parabolically vary with increasing numbers of carbon atoms in the n-alkyl chain, with a local minimum for the C(4) (n-butyl) analogue. A decrease in K(i) value between C(12) and C(13) was also observed. The statistical results for the best neural network fit of the 85 experimental K(i) values are r(2) = 0.84, rmsd = 0.39; r(cv)(2) = 0.68, and loormsd = 0.56. The generated neural network model with the 85 molecule training set may also be of value for future predictions of K(i) values for new virtual compounds, which can then be identified, subsequently synthesized, and tested experimentally.

Tris-azaaromatic quaternary ammonium salts: Novel templates as antagonists at nicotinic receptors mediating nicotine-evoked dopamine release

A series of tris-azaaromatic quaternary ammonium salts has been synthesized and evaluated for their ability to inhibit neuronal nicotinic acetylcholine receptors (nAChRs) mediating nicotine-evoked [(3)H]dopamine release from superfused rat striatal slices and for inhibition of [(3)H]nicotine and [(3)H]methyllycaconitine binding to whole rat brain membranes. The 3-picolinium compound 1,3,5-tri-{5-[1-(3-picolinium)]-pent-1-ynyl}benzene tribromide (tPy3PiB), 3b, exhibited high potency and selectivity for nAChR subtypes mediating nicotine-evoked [(3)H]dopamine release with an IC(50) of 0.2 nM and I(max) of 67%.

Bis-azaaromatic quaternary ammonium salts as antagonists at nicotinic receptors mediating nicotine-evoked dopamine release: An investigation of binding conformation

A series of conformationally restricted bis-azaaromatic quaternary ammonium salts (3 and 4) have been designed and synthesized in order to investigate the possible binding conformations of N,N'-dodecane-1,12-diyl-bis-3-picolinium dibromide (bPiDDB; 2), a compound which potently inhibits neuronal nicotinic acetylcholine receptors (nAChRs) mediating nicotine-evoked dopamine release. The preliminary structure-activity relationships of these new analogues suggest that bPiDDB binds in an extended conformation at the nAChR binding site, and that flexibility of the linker may be important for its high potency in inhibiting nAChRs mediating nicotine-evoked dopamine release.

Central Nicotinic Receptors: Structure, Function, Ligands, and Therapeutic Potential

The growing interest in nicotinic receptors, because of their wide expression in neuronal and non-neuronal tissues and their involvement in several important CNS pathologies, has stimulated the synthesis of a high number of ligands able to modulate their function. These membrane proteins appear to be highly heterogeneous, and still only incomplete information is available on their structure, subunit composition, and stoichiometry. This is due to the lack of selective ligands to study the role of nAChR under physiological or pathological conditions; so far, only compounds showing selectivity between alpha4beta2 and alpha7 receptors have been obtained. The nicotinic receptor ligands have been designed starting from lead compounds from natural sources such as nicotine, cytisine, or epibatidine, and, more recently, through the high-throughput screening of chemical libraries. This review focuses on the structure of the new agonists, antagonists, and allosteric ligands of nicotinic receptors, it highlights the current knowledge on the binding site models as a molecular modeling approach to design new compounds, and it discusses the nAChR modulators which have entered clinical trials.

Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 104 VMAT2 ligands with experimentally measured log(1/K(i)) values. A set of related descriptors, including topological, geometrical, GETAWAY, aromaticity, and WHIM descriptors, was selected to build nonlinear quantitative structure-activity relationships. A partial least squares (PLS) regression model was also developed for comparison. The nonlinearity of the relationship between molecular descriptors and VMAT2 ligand activity was demonstrated. The obtained neural network model outperformed the PLS model in both the fitting and predictive ability. ANN analysis indicated that the computed activities were in excellent agreement with the experimentally observed values (r(2)=0.91, rmsd=0.225; predictive q(2)=0.82, loormsd=0.316). The generated models were further tested by use of an external prediction set of 15 molecules. The nonlinear ANN model has r(2)=0.93 and root-mean-square errors of 0.282 compared with the experimentally measured activity of the test set. The stability test of the model with regard to data division was found to be positive, indicating that the generated model is predictive. The modeling study also reflected the important role of atomic distribution in the molecules, size, and steric structure of the molecules when they interact with the target, VMAT2. The developed models are expected to be useful in the rational design of new chemical entities as ligands of VMAT2 and for directing synthesis of new molecules in the future.