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

LC-MS analysis of nitroguanidine compounds by catalytic reduction using palladium modified graphitic carbon nitride catalyst

The analysis of compounds of the nitroguanidine family at trace level poses an analytical challenge. Nitroguanidine, 1-methyl-3-nitroguanidine, and 1-methyl-3-nitro-1-nitrosoguanidine, which are addressed in this article, have low lipophilicity, with log(K_ow) equal to -0.89, - 0.84, and 0.68, respectively, and as such are not amenable for preconcentration from water. Liquid-liquid extraction and SPE fail to concentrate them from water and it is also not possible to extract them by ion exchange resin even after a pH change. Nitroguanidine and 1-methyl-3-nitroguanidine nitramines are explosives of growing use and thereby growing environmental concern due to lower detonation sensitivity compared to RDX. A sensitive method for the determination of nitroguanidine, 1-methyl-3-nitroguanidine, and 1-methyl-3-nitroso-1-nitroguanidine by reduction to the respective amines and subsequent hydrophobization by derivatization with 4-nitrobenzaldehyde followed by LC-ESI-MS analysis is described. Reduction by sodium borohydride using palladium modified graphitic carbon nitride (Pd/g-C₃N₄) provided improved sensitivity compared to the traditional palladium modified activated carbon due to the lower adsorption of the reduction products on the carbon nitride substrate. The limit of detection of the method was 10 ng L^-1 for nitroguanidine, and repeated analyses of spiked effluents and contaminated spring water gave relative standard deviations of 8.8% and 6.5%, respectively. The findings illuminate the great promise of Pd/g-C₃N₄ as a reduction catalyst for the determination of challenging hydrophilic organic contaminants.

Inhibition of Human DNA Polymerases Eta and Kappa by Indole-Derived Molecules Occurs through Distinct Mechanisms

Overexpression of human DNA polymerase kappa (hpol κ) in glioblastoma is associated with shorter survival time and resistance to the alkylating agent temozolomide (TMZ), making it an attractive target for the development of small-molecule inhibitors. We previously reported on the development and characterization of indole barbituric acid-derived (IBA) inhibitors of translesion DNA synthesis polymerases (TLS pols). We have now identified a potent and selective inhibitor of hpol κ based on the indole-aminoguanidine (IAG) chemical scaffold. The most promising IAG analogue, IAG-10, exhibited greater inhibitory action against hpol κ than any other human Y-family member, as well as pols from the A-, B-, and X-families. Inhibition of hpol κ by IAG analogues appears to proceed through a mechanism that is distinct from inhibition of hpol η based on changes in DNA binding affinity and nucleotide insertion kinetics. By way of comparison, both IAG and IBA analogues inhibited binary complex formation by hpol κ and ternary complex formation by hpol η. Decreasing the concentration of enzyme and DNA in the reaction mixture lowered the IC₅₀ value of IAG-10 to submicromolar values, consistent with inhibition of binary complex formation for hpol κ. Chemical footprinting experiments revealed that IAG-10 binds to a cleft between the finger, little finger, and N-clasp domains on hpol κ and that this likely disrupts the interaction between the N-clasp and the TLS pol core. In cell culture, IAG-10 potentiated the antiproliferative activity and DNA damaging effects of TMZ in hpol κ-proficient cells but not in hpol κ-deficient cells, indicative of a target-dependent effect. Mutagenic replication across alkylation damage increased in hpol κ-proficient cells treated with IAG-10, while no change in mutation frequency was observed for hpol κ-deficient cells. In summary, we developed a potent and selective small-molecule inhibitor of hpol κ that takes advantage of structural features unique to this TLS enzyme to potentiate TMZ, a standard-of-care drug used in the treatment of malignant brain tumors. Furthermore, the IAG scaffold represents a new chemical space for the exploration of TLS pol inhibitors, which could prove useful as a strategy for improving patient response to genotoxic drugs.

Chemometric modeling of Daphnia magna toxicity of agrochemicals

Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

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.

Azine-Hydrazone Tautomerism of Guanylhydrazones: Evidence for the Preference Toward the Azine Tautomer

Guanylhydrazones have been known for a long time and have wide applications in organic synthesis, medicinal chemistry, and material science; however, little attention has been paid toward their electronic and structural properties. Quantum chemical analysis on several therapeutically important guanylhydrazones indicated that all of them prefer the azine tautomeric state (by about 3-12 kcal/mol). A set of simple and conjugated azines were designed using quantum chemical methods, whose tautomeric preference toward the azine tautomer is in the range of 3-8 kcal/mol. Twenty new azines were synthesized and isolated in their neutral state. Variable temperature NMR study suggests existence of the azine tautomer even at higher temperatures with no traces of the hydrazone tautomer. The crystal structures of two representative compounds confirmed that the title compounds prefer to exist in their azine tautomeric form.

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.