Pertinent Descriptors Research Articles

Molecular Docking and Molecular Dynamics Simulations of the Urease Inhibitory Activity of 2,3‐Dihydroxybenzohydrazide Derivatives

AbstractThis investigation focuses on a suite of 2,3‐dihydroxybenzoyl hydrazine derivative ligands, examining their viability as urease activity inhibitors and delving into their operative mechanisms. Utilizing molecular docking approaches, the binding patterns and interactive loci of 2,3‐dihydroxybenzoyl hydrazine derivatives with urease were elucidated. The origin of the binding forces between the ligands and urease, along with pertinent descriptors, were scrutinized through an amalgamation of density functional theory calculations and molecular dynamics simulations. The findings indicate that the HOMO‐LUMO energy gap, the dipole moment, and the electrostatic potential serve as robust descriptors of the inhibitory activity exhibited by 2,3‐dihydroxybenzoyl hydrazine derivative ligands against urease. The ligand designated BH‐k emerged as the most efficacious urease inhibitor, with critical hydrogen bonds forming between it and the residues His409, Ala636, and Asp633, in addition to hydrophobic interactions with Arg439. The design of ligands capable of establishing additional hydrogen bonds constitutes an effective strategy to amplify inhibitory efficacy. The results of this research aspire to lay a theoretical foundation for the advancement of novel urease inhibitor designs to counteract health issues emanating from aberrant urease activity.

Quantitative structure–activity relationship for anti-leukemia activity of 2,6-bis(arylidene)cyclohexanones derivatives using Free-Wilson approach

Quantitative structure-activity anti-leukemia relationships have been studied for a series of 2,6-bis(arylidene)cyclohexanones derivatives by means of multiple linear regression (MLR). The codification of 2,6-bis (arylidene) cyclohexanones derivatives is based on the Free-Wilson approach. The anti-leukemia activity against P388 leukemia cells of the compounds studied was well correlated with descriptors encoding the chemical structure. Using the pertinent descriptors revealed by a stepwise procedure in the multiple linear regression techniques, a correlation coefficient of 0.962 (s=0.19535) was obtained for this model. The results show that the EWG molecular descriptors, attached to the basic backbone of the 2,6-bis (arylidene) cyclohexanone derivatives promote the increase in the anti-leukemia activity log (1/IC50). While the EDG molecular descriptors, attached to the basic backbone of these same molecules, promote the decrease in this cytotoxic activity.

Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides

The partitioning tendency of pesticides, in this study herbicides in particular, into different environmental compartments depends mainly of the physicochemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure for determining the aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, always associated with selection of a genetic algorithm (GA) of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series of 80 herbicides. The values of log S of the studied compounds were well correlated with the descriptors. Considering the pertinent descriptors, a Pearson correlation squared coefficient (R2) of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

Proton transfer reaction confined within carbon nanotubes: Density functional theory and quantitative structure–property relationship analysis

In this work, we focus our attention on chemical reactions confined within carbon nanotubes. As a result of the confinement within carbon nanotubes, novel physical and chemical properties are found for the confined materials. We consider the feasibility of proton transfer inside carbon nanotubes. To do that, we have chosen formamide as the simplest real model for exhibiting the tautomerization in DNA. We have used the quantitative structure–property relationship method, based on geometry optimization and quantum chemical structural descriptors, to illustrate the potential of using the confined space inside carbon nanotubes, which will provide comprehensive information about carbon nanotubes. All calculations have been carried out using density functional theory quantum calculations with the B3LYP functional. The geometries optimized by the Gaussian program were transferred to the computer software DRAGON to calculate pertinent descriptors that could be used in the quantitative structure–property relationship model.

Towards Quantitative Acoustic Emission by Finite Element Modelling: Contribution of Modal Analysis and Identification of Pertinent Descriptors

Acoustic emission (AE) is used for damage monitoring and health diagnosis of materials. Several experimental investigations have shown the aptitude of AE to identify signatures of damage mechanisms. Nevertheless, there is a lack of numerical modelling or simulation to understand the link between the source and the AE signals. Since the interpretation of data of AE measurements mainly relies on empirical correlation between the signal and the mechanical source, a detailed description of the effects of the different stages of the acquisition chain is still lacking. Moreover, the geometry of the specimen can strongly influence the propagation modes. In this study, we propose to model AE with the Finite Element Method, in order to investigate the effect of the type of damage, the geometry of the specimen and the piezoelectric sensor on the waves and on the AE parameters. After validating the model with an experimental pencil lead break, we perform a modal analysis on the numerical signals. This consists of identifying the excited modes for several sources using a 2D Fast Fourier Transform. The last part is devoted to the identification of pertinent descriptors with a perfect point contact sensor and with a resonant sensor.

Quantitative structure-activity relationships for cellular uptake of surface-modified nanoparticles.

Quantitative structure-activity relationships (QSARs) were developed, for cellular uptake of nanoparticles (NPs) of the same iron oxide core but with different surface-modifying organic molecules, based on linear and non-linear (epsilon support vector regression (ε-SVR)). A linear QSAR provided high prediction accuracy of R2=0.751 (coefficient of determination) using 11 descriptors selected from an initial pool of 184 descriptors calculated for the NP surfacemodifying molecules, while a ε-SVR based QSAR with only 6 descriptors improved prediction accuracy to R2=0.806. The linear and ε-SVR based QSARs both demonstrated good robustness and well spanned applicability domains. It is suggested that the approach of evaluating pertinent descriptors and their significance, via QSAR analysis, to cellular NP uptake could support planning and interpretation of toxicity studies as well as provide guidance for the tailor-design NPs with respect to targeted cellular uptake for various applications.

QSAR for Antimycobacterial Activity of β-Thia Adduct of Chalcone and Diazachalcone Derivatives

Quantitative structure-activity antimycobacterial relationships have been studied for a series of β-thia adduct of chalcone and diazachalcone derivatives by means of multiple linear regression (MLR) and artificial neural networks (ANN). The antimycobacterial activity against M. tuberculosis H37Rv of the compounds studied was well correlated with descriptors encoding the chemical structure. Using the pertinent descriptors revealed by a stepwise procedure in the multiple linear regression technique, a correlation coefficient of 0.9798 (s=0.0869) for the training set was obtained for the ANN model in a [3-3-1] configuration. The results show that the antimycobacterial activity of these compounds is strongly dependent on hydrogen-bonding donors, molecular refraction and also molecular connectivity indices for 2nd order.

Toward a general predictive QSAR model for gamma-secretase inhibitors

Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neural-network (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors.

Theoretical Study of the Anti-Human Immuno-Deficiency Virus TIBO Molecule Confined Into Carbon Nanotubes

This work deals with the butterfly conformation control of the anti-HIV TIBO molecule confined into carbon nanotubes (CNT). This theoretical study concerns the variation of some pertinent conformation descriptors such as butterfly angle, wingspan, volume, dipole moment, solvation energy and confinement energy versus carbon nanotube diameters. Obtained results show that it is possible to describe the configurations of actual drugs as 8-Cl or 9-Cl TIBO as the parent molecule TIBO encapsulated in an adequate CNT. Our approach indicates that drug confinement inside CNTs may be a promising way to use a same drug in order to fellow HIV virus mutations.

Radiological Assessment of Accelerated versus Traditional Approaches to Postoperative Rehabilitation following Matrix-Induced Autologous Chondrocyte Implantation.

Objective:To assess the safety and efficacy of accelerated compared with traditional postoperative weightbearing (WB) rehabilitation following matrix-induced autologous chondrocyte implantation (MACI) of the knee, using MRI.Methods:A randomized controlled study design was used to assess MRI-based outcomes of MACI grafts in 70 patients (45 men, 25 women) who underwent MACI to the medial or lateral femoral condyle, in combination with either traditional or accelerated approaches to postoperative WB rehabilitation. High-resolution MRI was undertaken and assessed 8 previously defined pertinent parameters of graft repair, as well as a combined MRI composite score at 3, 12, and 24 months postsurgery. The association between clinical and MRI-based outcomes, patient demographics, chondral defect parameters, and injury/surgery history was investigated.Results:Both groups significantly improved (P < 0.05) in the MRI composite score and pertinent descriptors of graft repair throughout the postoperative period until 24 months postsurgery. There were no differences (P > 0.05) observed between the 2 groups. Patient age, body mass index, chondral defect size, and duration of preoperative symptoms were significantly correlated (P < 0.05) with several MRI-based outcomes at 24 months, whereas there were no significant pertinent correlations (P > 0.05) observed between clinical and MRI-based outcomes.Conclusion:The accelerated WB approach was not detrimental to graft development at any stage throughout the postoperative assessment timeline from baseline to 24 months postsurgery and may potentially accelerate patient return to normal function, while reducing postoperative muscle loss, intra-articular adhesions, and associated gait abnormalities.

A Quantitative Structure-Activity Relationship Model

Structure-activity relationships were studied for a series of 46 2.6-dimethyl-3.5-dicabomethoxy-4-phenyl-1.4-dihydropyridine derivatives by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The values of log (1/EC50), which represents the 50% effective concentration for blocking the Ca2+ channel of the studied compounds were correlated with the descriptors encoding the chemical structures. Using the pertinent descriptors revealed by the regression analysis, a correlation coefficient of 0.99 (s = 0.23) for the training set (n = 46) was obtained for the ANN using the Levenberg-Marquardt algorithm with a 3-10-1 configuration. The results obtained from this study indicate that the activity of 2.6-dimethyl-3.5-dicabomethoxy-4-phenyl-1.4-dihydropyridine derivatives is strongly dependent on molar refractivity (MR), electronic factors (especially on the connectivity indices (IC0)) and hydrogen-bond donor's (HBD) of the molecule. Comparison of the descriptor's contribution obtained with MLR and ANN models indicates the presence of non-linearity in the data and the interaction effect between them since the efficiency of these descriptors was increased by the ANN model.On the other hand, we have used a new, robust structure-activity mapping technique, a Bayesian-regularized neural network, to develop a quantitative structure-activity relationship (QSAR) model, and the ability of the model was tested by using the cross validation technique. The results show that the method is robust and reliable and gives good results. Comparisons of Bayesian neural net models with those derived by classical MLR model analysis showed its superiority in generalization.

In Silico and In Vitro Filters for the Fast Estimation of Skin Permeation and Distribution of New Chemical Entities

The development of in silico and in vitro tools to estimate or predict the passive human skin permeation and distribution of new chemical entities, useful in dermal drug delivery, in absorption studies of toxic compounds, and in the cosmetics industry, is presented. In vitro permeation parameters were measured using the artificial membrane PAMPA-skin. The Volsurf approach was then applied to extract pertinent descriptors from molecular interaction fields characterizing the molecular structure of tested compounds. Two useful three-dimensional solvatochromic models able to predict PAMPA permeation parameters directly from the molecular structure were obtained using the partial least squares analysis. The models also provide valuable information to understand the link between physicochemical and structural properties of tested compounds and their interactions with the artificial membrane PAMPA-skin and can be useful to rapidly estimate their permeation through the human skin.

Uso dos recursos de Internet na Enfermagem: uma revisão

Os recursos do sistema Internet estão cada vez mais popularizados em todas as áreas das atividades humanas na sociedade. Este artigo teve como objetivo identificar e descrever as principais modalidades de utilização da Internet pela enfermagem nas áreas de Educação, Assistência e Pesquisa. Trata-se de um estudo bibliográfico cuja fonte de dados foi a base de dados MEDLINE, usando-se expressões de pesquisa pertinentes às áreas enfocadas. Dos trabalhos referidos, 63 artigos foram selecionados. A principal utilização na área de Educação é o desenvolvimento de aplicações para promover ensino à distância; na área de Educação ao Paciente é a disponibilização de informação de saúde; na área de Assistência são as aplicações que oferecem protocolos assistenciais para promover a prática baseada em evidências e na área de Pesquisa é a coleta de dados on-line. Conclui-se que a enfermagem vem utilizando satisfatoriamente os recursos do sistema Internet e, isto tem contribuído para o próprio desenvolvimento da profissão.

QSAR for anti-malarial activity of 2-aziridinyl and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives

Quantitative structure-activity antimalarial relationships have been studied for 63 analogues of 2-aziridinyl and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives by means of multiple linear regression (MLR) and artificial neural networks (ANN). The antimalarial activity [-log(IC50x10(6))] of the compounds studied were well correlated with descriptors encoding the chemical structure. Using the pertinent descriptors revealed by a stepwise procedure in the multiple linear regression technique, a correlation coefficient of 0.9394 (s=0.2121) for the training set was obtained for the ANN model in a [3-5-1] configuration. The results show that the antimalarial activity of 2-aziridinyl and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives is strongly dependent on hydrophobic character, hydrogen-bond acceptors and also steric factors of the substituents.

Quantitative structure-diastereoselectivity relationships for arylsulfoxide derivatives in radical chemistry.

Quantitative structure-diastereoselectivity relationships were studied for the intermolecular radical addition of deuterium and allyltributyltin to chiral arylsulfoxides by means of multiple linear regression and artificial neural networks (ANN). The values of diastereoselectivity (% syn) of the compounds studied were well correlated with the descriptors encoding the chemical structure. Using the pertinent descriptors revealed by the regression analysis, a square correlation coefficient of 0.9577 ( s=5.3825) for the training set was obtained for the ANN model in a 2-4-1 configuration. The results obtained from this study indicate that the diastereoselectivity of arylsulfoxide derivatives is strongly dependent on the shape of the R and X groups. FIGURE General structure of alpha-sulfinyl radicals

Structure-cytotoxicity relationships for a series of HEPT derivatives.

Structure-cytotoxicity relationships were studied for a series of 90 HEPT derivatives by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The values of log(1/CC50) (CC50=cytotoxic dose of compound required to reduce the proliferation of normal uninfected MT-4 cells by 50%) of the studied compounds were correlated with the descriptors encoding the chemical structures. Using the pertinent descriptors revealed by the regression analysis, a correlation coefficient of 0.935 ( s=0.149) for the training set ( n=81) was obtained for the ANN model with a 5-6-1 configuration. The results obtained from this study indicate that the cytotoxicity of HEPT derivatives is strongly dependent on hydrophobic factors, mainly log P(R1), and dependent on the steric factors, especially SigmaMW(R3+R4). Comparison of the descriptors' contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to cytotoxicity may be non-linear.

Structure–camphor odour relationships using the Generation and Selection of Pertinent Descriptors approach

Structure–camphor odour relationships were carried out using GSPD (Generation and Selection of Pertinent Descriptors) or GESDEM (Genération et Sélection de Descripteurs et Elaboration de Motifs). This methodology consists in constructing descriptors from fragments. Their size (called order) is increased stepwise to obtain an optimum order giving a best classification. The set of studied compounds included 99 aliphatic alcohols, for which olfactory properties have been described by Schnabel et al. [K.O. Schnabel, H.D. Belitz, C.V. Ranson, Untersuchungen zur Struktur–Aktivitats–Beziehung bei Geruchsstoffen, Z. Lebensm.-Unters.-Forsch. 187 (1988) 215–223]. Classification of compounds was tested by means of artificial neural network (NN) with back-propagation algorithm, Kth nearest neighbour (KNN) and discriminant analysis (DA). After GSPD, all compounds were well-classified and 93% of them were well-predicted by means of leave-one-out method using NN. A new test sample composed of 42 alcohols (25 camphor and 17 non-camphor) with structures analogue to these of training set was collected from Belstein and trained on weight matrix of network which served in classification phase. The results of prediction were at 90.5% in agreement with those of literature.

Comparison of Tetrahymena and Pimephales Toxicity Based on Mechanism of Action

The toxicity data of 256 chemicals tested in both the 96-h Pimephales promelas mortality assay and the 2-d Tetrahymena pyriformis growth inhibition assay were evaluated using quantitative structure-activity relationships (QSARs). Each chemical was a priori assigned a mode of action of either narcoses or soft electrophilicity. Narcoses were separated into nonpolar narcosis, polar narcosis, monoester narcosis, diester narcosis, amine narcosis, and weak acid respiratory uncoupling based on the presence or absence of specific toxicophores. Toxicity of each narcotic mechanism was initially regressed against the 1-octanol-water partition coefficient (log K ow). The slopes of these log K ow based QSARs were observed to ascertain whether a relationship exists between the value of the slope and the reactivity of the mechanism of action. With both the fish and ciliate data nonpolar narcosis was the least reactive mechanism. It was followed by the other reversible narcoses. The soft electrophile mode was separated into the specific molecular mechanisms of: S N2 reactors, Schiff-base formers, Michael-type addition, or proelectrophilicity (precursors to Michael-type addition chemicals). These mechanisms were represented structurally by the nitrobenzenes, aldehydes, polarized α-β unsaturates (e.g., acrylates and methacrylates), and acetylenic alcohols, respectively. Electrophilic toxicity was not correlated with hydrophobicity. QSARs based on molecular orbital (MO) quantum chemical descriptors were used to improve the predictability of the electrophilic mechanisms. Relevant descriptors include average superdelocalizability (S n av) for the nucleophilic addition of the nitrobenzene; atom x and y acceptor superdelocalizability (A x ); and bond order (B x-y ) for the Michael-type addition of the acrylates; and log K ow and atom x net charge (Q x ) for the Schiff-base forming aldehydes. The pertinent descriptors for proelectrophiles were log K ow and S n av. Principal differences between the QSARs for the two biological endpoints were observed for the ester narcoses, proelectrophiles, and Schiff-base forming aldehydes.

Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network.

Structure-toxicity relationships were studied for a set of 44 herbicides by means of principal component analysis (PCA), multiple regression analysis (MRA), and neural network (NN). The values of log LD50 (lethal dose 50, acute, oral, rat) of the studied compounds were well correlated with the descriptors encoding the chemical structures. Considering the pertinent descriptors, a correlation coefficient of 0.90 (n = 41) was obtained for the NN model with a configuration of 4-3-1 (and 0.92 (n = 41) with a configuration of 4-5-1). To evaluate the contribution of each descriptor on the activity, log LD50 was calculated by removing each descriptor a part. This approach provides the tendency of a descriptor to be favourable (or not) to the activity.