First Report on Screening of the Profiles of the Essential Oils and Volatiles from the Aerial Parts of Marrubium persicum Using Classical and Advanced Methods Prior to Gas Chromatographic Mass Spectrometric Determination

The Lamiaceae family consists of a broad spectrum of medicinal plants involving Marrubium L. genus. Regarding the diverse pharmaceutical uses of the plants belonging to this genus, they can be considered as proper alternatives for chemical drugs having harmful effects. The present work aims to identify and characterize chemical compositions of the essential oils and volatiles from the aerial partsof Marrubium persicum C. A. Mey. as an herbal plant in Iran using classical hydrodistillation. To establish a comprehensive comparison between the traditional techniques and advanced ones, microwave-based extraction techniques namely MAHD as well as SFME have also been utilized. In another part of this project, the profiles related to the volatile fractions from the aerial parts of Marrubium persicum C. A. Mey.have been assessed and compared with the other categories. The main components in the hydrodistillation (HD) method were α-pinene (21.5%), spathulenol (19.5%), α-thujene (17.4%), while the headspace solid-phase microextraction(HS-SPME) profile mainly consisted of β-caryophyllene (14%), eugenol (11.2%) and methyl eugenol (10.2%). On the other hand, using the SFME approach spathulenol (25.4%), α-pinene (17.4%) and germacrene D (9.5%) were found as the most abundant constituents. Moreover, in the MAHD profile caryophyllene oxide (13.1%), δ-elemene (12.4%), camphene (8.5%) were respectively the predominant natural compounds. According to gas chromatographic-mass spectrometric determinations, a total of 40 compounds were recognized in the corresponding profiles totally covering 94.6-99.7% of the whole chemical compositions. Sesquiterpene hydrocarbons were recognized as the most frequent groups of natural compounds in the profiles of the advanced approaches, whereas in the traditional one monoterpene hydrocarbons were found to be the dominant constituting group

Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)

A reliable quantitative structure retention relationship (QSRR) study has been evaluated to predict the retention indices (RIs) of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated), on an HP-5MS fused silica column. A principal component analysis showed that seven compounds lay outside of the main cluster. After elimination of the outliers, the data set was divided into training and test sets involving 80 and 28 compounds. The method was tested by application of the particle swarm optimization (PSO) method to find the most effective molecular descriptors, followed by multiple linear regressions (MLR). The PSO-MLR model was further confirmed through “leave one out cross validation” (LOO-CV) and “leave group out cross validation” (LGO-CV), as well as external validations. The promising statistical figures of merit associated with the proposed model (R²_train=0.936, Q²_LOO=0.928, Q²_LGO=0.921, F=376.4) confirm its high ability to predict RIs with negligible relative errors of predictions (REP train=4.8%, REP test=6.0%)

Ionic Strength dependence of Formation Constants: Complexation of Cobalt(II) With thiazolyl blue formazan in a Non-Ionic Micellar Medium

This paper describes the complexation of cobalt(II) with thiazolyl blue formazan in a micellar medium at the pH range 3 to10, using Spectrophotometric techniques. The influences of pH and surfactant amount have been also tested. The composition of the complex was determined by the continuous variation (Jobmethod) and mole-ratio methods. It was concluded that Co2+ forms a mononuclear 2:1 complex with thiazolyl blue formazan of the type CoL2- at pH = 6. Finally, the formation constants of the complex were evaluated at 25 ºC under ionic strengths ranging from 0.1 to 1 mol dm-3 of potassium nitrate

Novel QSPR Study on the Melting Points of a Broad Set of Drug-Like Compounds Using the Genetic Algorithm Feature Selection Approach Combined With Multiple Linear Regression and Support Vector Machine

A robust and reliable quantitative structure-property relationship (QSPR) study was established to forecast the melting points (MPs)  of a diverse and long set including 250 drug-like compounds. Based on the calculated descriptors by Dragon software package, to detect homogeneities and to split the whole dataset into training and test sets, a principal component analysis (PCA) approach was used. Accordingly, there was no outlier in the constructed cluster. Afterwards, the genetic algorithm (GA) feature selection strategy was used to select the most impressive descriptors resulting in the best-fitted models. In addition, multiple linear regression (MLR) and support vector machine (SVM) were used to develop linear and non-linear models correlating the molecular descriptors and the melting points. The validation of the obtained models was confirmed applying cross validation, chance correlation along with statistical features associated with external test set. Our computational study exactly showed a determination coefficient and of 0.853 and a root mean square error (RMSE) of 11.082, which are better than those MLR model (R2=0.712, RMSE 15.042%) accounting for higher capability of SVM-based model in prediction of the theoretical values related to melting points. In fact, using the GA approach resulted in selection of powerful descriptors having useful information concerning effective variables on MPs, which can be utilized in further designing of drug-like compounds with desired melting points

Solvent effects on protonation, chellation and stability constants of Pd(II)- 4-2-(pyridyl azo)-resorcinol in aqueous, mixed organic solvents and non-ionic micellar media

This paper is a descriptive report focusing on the trends of complex formation of palladium(II) and 4-2-(pyridyl azo)-resorcinol in different media under optimized conditions by using Uv/vis. spectrophotometric technique. All the affecting chemical variables have been investigated. At first, by application of job and mole ratio methods the stoichiometric M:L ratio was confirmed to be 1:1. By calculation the mean molar absorptivity coefficient and equilibrium concentration of complex, its stability constants were determined over a wide range of ionic strengths in presence of KCl as the background salt. The results showed the thermodynamically endurance of the complex and applicability of utilization of organic solvents. Satisfactory agreement between experimental and theoretical formation constants verifies the accuracy of the proposed method

Ethnobotany and phytochemistry of the Genus Eremostachys Bunge

In this review, the species from the genus Eremostachys Bunge are described and explored from different standpoints. In particular, the main attention is focused on phytochemistry also with reference to the biogenesis of the most important class of chemotaxonomic marker, the iridoids, and their co-occurrence with volatile terpene components of essential oils which own the same biogenetic precursors. The ethnopharmacological implications of the plants belonging to this genus are also reported in detail. Nevertheless, a few morphological and botanical details of Eremostachys are also presented, as well as some topics about its chemotaxonomy and pure pharmacology. Based on the data reported in the literature, different species of the genus Eremostachys show important and interesting peculiarities under all these aspects that are extensively discussed and commented

Application of genetic algorithm with multiple linear regressions for prediction of medicinal activity pyrazole derivatives

Quantitative structure-activity relationship (QSAR) study for prediction of medicinal activity of pyrazole derivatives is developed using structural descriptors and multiple linear regression (MLR) method. Molecular descriptors are selected by genetic algorithm. Then a simple, strong, descriptive and interpretable model with low error and high correlation coefficient is construct. The results illustrated that the linear techniques such as MLR combined with a successful variable selection procedure are capable to generate an efficient QSAR model for predicting the activity of different compounds. This model was used for the prediction of activity values of some medicinal compounds which were not used in the modeling procedure

An overview of the genus Aloysia Paláu (Verbenaceae). Essential oil composition, ethnobotany and biological activities

Aloysia Pal au is an important herbal genus from the Verbenaceae family and possesses numerous remedial properties in the folk medicine of Asian, European, and, in particular, South American countries. Only a few reports have discussed some phytochemical characteristics associated with Aloysia species. Right the lack of an exhaustive report prompted us to organize this review article. Accordingly, besides the ethnobotanical knowledge of Aloysia species, their essential oil profiles, phytochemistry of the polar isolated fractions, and the relevant biological activities are discussed in detail

A quantitative structure–activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors agonists

Quantitative structure–activity relationship (QSAR) of tetrabutyl­phosphonium bromide (TBPB) analogs as muscarinic acetylcholine receptors (mAChRs) agonists was studied. A suitable set of molecular descriptors was calculated and stepwise multiple linear regression (SW-MLR) was employed to select those descriptors that resulted in the best fitted models. A MLR model with three selected descriptors was obtained. Furthermore, the MLR model was va­lidated using the leave-one-out (LOO) and leave-group-out (LGO) cross-vali­dation, and the Y-randomization test. This model, with high statistical signifi­cance (R2train = 0.982, F = 388.715, Q2LOO = 0.973, Q2LGO = 0.977 and R2test = 0.986) could predict the activity of the molecules with a percentage predic­tion error lower than 5 %