An attempt to incorporate effect of direct interaction between a ligand and explicit water molecules into MM/3D‐RISM

Endpoint methods using continuum‐solvent models are widely used to estimate protein–ligand affinity. A recently developed method, MM/3D‐RISM, estimates the solvation energy using statistical mechanics by 3D‐RISM. This method is theoretically expected to accurately describe solvation effects and to also be less dependent on protein–ligand systems. In this study, we examined the performance of MM/3D‐RISM for a set of α‐thrombin inhibitors with a non‐congeneric series of ligands, containing three diverse chemical scaffolds. The standard MM/3D‐RISM showed a weak correlation (R² = 0.191) but correctly estimated affinity for two of the three scaffolds. However, the simplest inhibitor, benzamidine, was not ranked appropriately. From visual inspection of inhibitor‐binding modes, an attempt was made to incorporate the direct interaction between a ligand and water molecules into MM/3D‐RISM. A model (Model‐1) dealing with directly interacting water molecules (Wat) as an independent component of a protein (R)–ligand (L) complex‐formation, that is, R + L + Wat → R–L–Wat, showed a better linearity (R² = 0.422) than that of the standard MM/3D‐RISM model and achieved a good ranking of all three scaffolds of α‐thrombin inhibitors. Additionally, an attempt was made to model avidin–biotin system with a congeneric series of inhibitors, and results showed that both the standard MM/3D‐RISM model (R² = 0.839) and Model‐1 (R² = 0.695) satisfactorily estimated the affinity.     

Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies

Plant‐based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D‐QSAR analysis using k‐nearest neighbour molecular field analysis via stepwise forward–backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q² = 0.64 and pred_r² = 0.56). Also, a group‐based QSAR (G‐QSAR) model was developed based on partial least squares regression combined with stepwise forward–backward variable selection method. It gave best parametric results (r² = 0.74, q² = 0.56 and pred_r² = 0.54) which implied that the model is highly predictive. 3D‐QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G‐QSAR provided site‐specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.     

Pharmacophore modeling of some novel indole β-diketo acid and coumarin-based derivatives as HIV integrase inhibitors

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole β-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² = 0.81 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² = 0.69 for a randomly chosen test set of eight compounds. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may helps in the design and development of novel integrase inhibitors.     

In Silico Design of Beta‐Secretase Inhibitors in Alzheimer's Disease

Alzheimer's disease is the major cause of senile dementia, flewing out 10% of 65 years old and 50% of 85 years old global population. The major fisiopathologic characteristics of Alzheimer's disease are the deposition of extracellular neuritic plaques and the presence of intracellular neurofibrillary tangles in memory‐related areas of the brain. The plaques are composed by the β‐amyloid peptide with 40 or 42 residues, result from hydrolysis of the amyloid precursor protein by the β‐secretase 1 (BACE‐1) on the amyloidogenic pathway, that begins with the BACE‐1 and which inhibition is considered one of the most promising treatments available of Alzheimer's disease. In this work, molecular modeling techniques such as virtual screening of compound libraries, pharmacophore‐based virtual screening, molecular interaction fields, ADME/Tox predictions, and similarity search were used to design novel inhibitors of BACE‐1, starting from structures available in the Protein Data Bank. The results obtained from all virtual screening techniques leaded to 10 promising compounds, which were then evaluated by enzymatic assays, and, three of them showed inhibitory activity of BACE‐1 at a 1 μm range.     

Patent Update: Antibacterial Patents Appearing in the First Quarter of 1991

Introduction: When two novel aspartyl proteases were published in 1999 and 2000, beta-site APP-cleaving enzyme 1 (BACE1) was confirmed as the long sought after beta-secretase and Alzheimer's disease drug target. However, the role of its paralogue, BACE2, proved elusive until a 2011 publication implicated it as a Collectrin (TMEM27) secretase controlling pancreatic beta-cell proliferation and a new therapeutic intervention for diabetes.

Areas covered: This review, using SureChemOpen, encompasses early validation compounds and small-molecule BACE2 inhibitors for diabetes. Since 2010, one assay patent and several chemical series have been published by Roche but these were followed by filings from Novartis and Schering in 2012. The patents from these three companies include BACE2-only filings but also some specifying both BACE1 and BACE2 inhibitors.

Expert opinion: Roche's early collaborative target validation has given them a lead in BACE2 medicinal chemistry. However, the extensive data output for BACE1 in patents and papers over the last decade, plus liganded crystal structures for both proteases, should expedite the design of BACE2 inhibitors by other organisations. This may also shorten the development time for clinical candidates that, unlike those now entering Phase I trials for BACE1, would not need to be brain-penetrant.     

Prediction of anti-HCV activity of thiourea derivatives: QSAR approach

Abstract  

In pursuit of more active thioureas as anti-HCV agent, QSAR studies were performed on a series of thioureas to explore the various physico-chemical parameters responsible for their activity against HCV-infected AVa 5 cell. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was performed to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR model was having correlation coefficient (R) = 0.902 and cross-validated squared correlation coefficient (Q ²) = 0.734. The developed significant QSAR model indicates that hydrophobicity, dielectric energy, valence connectivity index (order 1), and ionization potential of whole molecule play an important role in anti-HCV activity of thioureas. The hydrophobicity of thioureas was found to have parabolic relation with its anti-HCV activity. The optimum logP value for these anti-HCV compounds was found to be 4.988.     

Kinetics and molecular docking studies of fucosterol and fucoxanthin,BACE1 inhibitors from brown algae Undaria pinnatifida and Ecklonia stolonifera

Since the action of β-site amyloid precursor protein cleaving enzyme 1 (BACE1) is strongly correlated with the onset of Alzheimer's disease (AD), the development of BACE1 inhibitors as therapeutic agents is being vigorously pursued. In our ongoing research aimed at identifying anti-AD remedies derived from maritime plants, we evaluated the BACE1 inhibitory activities of fucosterol and fucoxanthin from Ecklonia stolonifera and Undaria pinnatifida. In vitro anti-AD activities were performed via BACE1 inhibition assays, as well as enzyme kinetic and molecular docking predictions. Based on enzyme-based assays, fucosterol and fucoxanthin showed noncompetitive and mixed-type inhibition, respectively, against BACE1. In addition, docking simulation results demonstrated that the Lys224 residue of BACE1 interacted with one hydroxyl group of fucosterol, while two additional BACE1 residues (Gly11 and Ala127) interacted with two hydroxyl groups of fucoxanthin. Moreover, the binding energy of fucosterol and fucoxanthin was negative (−10.1 and −7.0 kcal/mol), indicating that hydrogen bonding may stabilize the open form of the enzyme and potentiate tight binding of the active site of BACE1, resulting in more effective BACE1 inhibition. The results suggest that fucosterol and fucoxanthin may be used beneficially in the treatment of AD and provide potential guidelines for the design of new BACE1 inhibitors.     

Study on the quantitative structure–toxicity relationships of benzoic acid derivatives in rats via oral LD50

Quantitative structure–toxicity relationship (QSTR) studies play an important role in toxicity forecasting, which is widely used in the study of modern compounds. Benzoic acid derivatives are an important type of organic chemicals. Most of them may cause serious public health and environmental problems. The effect of quantum-chemistry parameters on the toxicity of benzoic acid derivatives in rats via oral 50% lethal dose (LD₅₀) was studied by QSTR, and a model to predict the toxicity of benzoic acid derivatives was constructed. In order to obtain an accurate model, cross factors were considered and a model for predicting the toxicity of benzoic acid derivatives in rats via oral LD₅₀ was built using a linear regression method (correlation 0.990). The novel model is - log^toxi = 0.144\textLogP - 0.0269SAG + 0.0000127HoF - 0.000377PE - \log^{toxi} = 0.144{\text{Log}}P - 0.0269SAG + 0.0000127HoF - 0.000377PE , R ² = 0.990, C(p) = 4.000, mean square error (MSE) = 0.785. The model demonstrated good forecasting ability.     

Two- and three-dimensional QSAR studies on benzyl amide-ketoacid inhibitors of HIV integrase and their reduced analogues

The discovery of clinically relevant inhibitors of HIV-integrase for antiviral therapy has proven to be a challenging task. Ketoacid-derived inhibitors selectively inhibit the strand transfer reaction of HIV-integrase. To elucidate the structural properties required for HIV-integrase inhibitory activity, we present here the results of two- and three-dimensional (2D and 3D) QSAR studies of a series of 24 benzyl amide derivatives. The 2D-QSAR studies were performed using three methods: the multiple linear regression method (MLR), giving r ² = 0.9340 and q ² = 0.8471; the partial least squares method, with r ² = 0.9291 and q ² = 0.7896; and principle component analysis, giving r ² = 0.6496 and q ² = 0.3893. The 3D-QSAR studies were performed using the stepwise variable selection k-nearest-neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient (q ²) of 0.4577 and a non-cross-validated correlation coefficient (r ²) of 0.5836 were obtained. Contour maps using this approach showed that steric effects dominantly determine binding affinities. The information rendered by 2D and 3D QSAR models may lead to a better understanding of structural requirements of HIV-integrase inhibitors and can help in the design of novel potent molecules.     

<Superscript>1</Superscript>H NMR metabolomics of earthworm responses to polychlorinated biphenyl (PCB) exposure in soil

¹H NMR-based metabolomics was used to examine the metabolic profile of D₂O-buffer extracted tissues of Eisenia fetida earthworms exposed for 2 days to an artificial soil spiked with sub-lethal concentrations of polychlorinated biphenyls (PCBs) (0, 0.5, 1, 5, 10, or 25 mg/kg Aroclor 1254). Univariate statistical analysis of the identified metabolites revealed a significant increase in ATP concentration in earthworms exposed to the highest soil PCB concentration, but detected no significant changes in other metabolites. However, a multivariate approach which considers alterations in multiple metabolites simultaneously, identified a significant linear relationship between earthworm metabolic profiles and PCB concentration (cross-validated PLS-regression with 7 components, R²X = 0.99, R²Y = 0.77, Q²Y = 0.45, P < 0.001). Significant changes in pair-wise metabolic correlations were also detected as PCB concentration increased. For example, lysine and ATP concentrations showed no apparent correlation in control earthworms (r = 0.22, P = 0.54), but were positively correlated in earthworms from the 25 mg/kg treatment (r = 0.87, P = 0.001). Overall, the observed metabolic responses suggest that PCBs disrupted both carbohydrate (energy) metabolism and membrane (osmolytic) function in E. fetida. The ability of ¹H NMR-based metabolomics to detect these responses suggests that this method offers significant potential for direct assessment of sub-lethal PCB toxicity in soil.     

Which factors predict the time spent answering queries to a drug information centre?

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R ² = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R ² = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries.     

Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity

Berberine (BBR), isolated from a Chinese herb, is identified as a new cholesterol‐lowering small molecule, and hundreds of berberine derivatives have been obtained for optimization of their hypolipidemic activities in recent years. However, so far there is no available quantitative structure–activity relationship (QSAR) model used for the development of novel BBR analogues with hypolipidemic activities, mainly due to lack of lipid‐lowering molecular mechanisms and target identification of BBR. In this paper, the tactics using ligand efficiency indice instead of pIC₅₀ as the activity could be adopted for the development of BBR QSAR models. A series of 59 BBR derivatives with hypolipidemic activities have been studied and split randomly into three sets of training and test sets. Statistical quality of most building models shows obviously robust. Best calculated model that employs LLE indice as the activity (Model 6 ) has the following statistical parameters: for training set R² = .984, Q² = 0.981, RMSE = 0.1160, and for test set R² = .989, RMSE = 0.0067. This model would be used for the development of novel BBR analogues with lipid‐lowering activities as a hit discovery tool.     

miR‐222/GAS5 is involved in DNA damage and cytotoxic effects induced by temozolomide in T98G cell line

Temozolomide (TMZ), a therapeutic DNA alkylator that can cause lethal DNA damage in cancer cells, is widely used for the standard chemotherapy against glioblastoma. However, long‐term treatment with TMZ often causes drug resistance and poor prognosis, the mechanism of which remains largely unclear. This study aimed to investigate the possible role of miR‐222/GAS5 axis on DNA damage and cytotoxic effects induced by TMZ in glioblastoma cells (T98G). Data suggest that the DNA comet tail length of T98G is positively correlated with the levels of miR‐222 (R² = 0.9808, P < 0.05), and negatively correlated with the levels of GAS5 (R² = 0.8903, P < 0.05). The optical density value of T98G is negatively correlated with the levels of miR‐222 (R² = 0.7848, P < 0.05), and positively correlated with the levels of GAS5 (R² = 0.6886, P < 0.05). Furthermore, comet tail length and optical density value are negatively and positively correlated with the levels of O‐6‐methylguanine‐DNA methyltransferase, respectively (R² = 0.8462, P < 0.05; R² = 0.7018, P < 0.05). In conclusion, miR‐222/GAS5 is involved in DNA damage and cytotoxic effects induced by TMZ, which means that miR‐222/GAS5 may have great potential of being used as a biomarker for screening of chemotherapeutic alkylators.     

Molecular Factor Computing for Predictive Spectroscopy

Purpose The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Methods Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0–14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. Results In engineering simulations, four MFC filters gave an adequate calibration model (r² = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r² = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. Conclusion MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.