Interaction of Naproxen with Crystalline and Amorphous Methylated β-Cyclodextrin in the Liquid and Solid State

Abstract

Interactions of naproxen (NAP) with amorphous, randomly methylated β-cyclodextrin at a degree of substitution per anhydroglucose unit of 1.8 (RAMEB) and with crystalline heptakis-(2,6-di-O-methyl)-β-cyclodextrin (DIMEB) were studied in aqueous solution and in the solid state using, respectively, phase-solubility analysis (at 25 °C, 37 °C and 47 °C) and differential scanning calorimetry (DSC) supported by X-ray powder diffractometry. RAMEB and DIMEB displayed similar solubilizing and complexing abilities towards NAP, suggesting analogous inclusion modes of the drug in the host cavity in aqueous solution. Differences were instead observed in interactions in the solid state, where the amorphizing capacity of RAMEB toward NAP (evaluated by DSC) was about twice that of DIMEB at each drug-to-carrier ratio. Assuming that inclusion complexation is also involved in solid-state interactions, molecular modelling accounted for the experimental results in terms of structural features of DIMEB, i.e. the particular inwards orientation of O-6-C-8 groups of three alternate glucoses on the primary hydroxyl side which hampers a deep penetration of NAP in the DIMEB cavity in the solid state. On the contrary, no obstruction of the cavity apparently occurs with RAMEB due its noncrystalline state. The aqueous dissolution rate of NAP from NAP-RAMEB and NAP-DIMEB blends containing 0.59, 0.73, 0.85, and 0.92 mass fraction of carrier linearly increased at decreasing drug-to-carrier ratios. The improvement was 5 to 20 times (from powders) and 50 to 200 times (from discs) the dissolution rate of NAP alone for both carrier. Therefore the choice of the amorphous RAMEB in pharmaceutical formulations can be recommended mainly for economic reasons, though the anhydrous and non-hygroscopic nature of crystalline DIMEB might be of particular advantage in case of moisture sensitive formulations.     

Investigation of the complexation of essential oil components with cyclodextrins

The formation of inclusion complexes of six essential oil (EO) components (β-caryophyllene, cis-ocimene, trans-ocimene, sabinene hydrate (thujanol), γ-terpinene and α-terpineol) with six cyclodextrins (CDs) (α-CD, β-CD, γ-CD, HP-β-CD, RAMEB and CRYSMEB) was investigated by using static headspace-gas chromatography and UV–visible spectroscopy. Retention studies showed that CDs could efficiently reduce the volatility of EO components except for β-caryophyllene with α-CD. In this case, no inclusion complex was detected while for other compounds the formation of 1:1 inclusion complexes was observed. Results revealed that the inclusion stability mainly depends on geometric complementarity between encapsulated molecule and CD's cavity. Molecular modelling was used to investigate the complementarities between host and guest. Thus, CDs could efficiently be regarded as promising encapsulants for EO components leading to improve their application in cosmetic, pharmaceutical and agriculture fields.     

ToxML,a data exchange standard with content controlled vocabulary used to build better (Q)SAR models

Development of accurate quantitative structure–activity relationship (QSAR) models requires the availability of high quality validated data. International regulations such as REACH in Europe will now accept (Q)SAR-based evaluations for risk assessment. The number of toxicity datasets available for those wishing to share knowledge, or to use for data mining and modelling, is continually expanding. The challenge is the current use of a multitude of different data formats. The issues of comparing or combining disparate data apply both to public and proprietary sources. The ToxML project addresses the need for a common data exchange standard that allows the representation and communication of these data in a well-structured electronic format. It is an open standard based on Extensible Markup Language (XML). Supporting information for overall toxicity endpoint data can be included within ToxML files. This makes it possible to assess the quality and detail of the data used in a model. The data file model allows the aggregation of experimental data to the compound level in the detail needed to support (Q)SAR work. The standard is published on a website together with tools to view, edit and download it.     

QSAR Study on Binding Affinity of PATs (Rodenticides) to the [3H]-Mepyramine-Labelled H1 Receptor In Rat and Guinea Pig Brain

Abstract

The binding of a series of PAT analogues (rodenticides) to the [³H]-mepyramine-labelled H₁ receptor in rat and guinea pig brain was investigated topologically using negentropy (N), molecular redundancy (MRI), first-order molecular connectivity (¹X _v ), Wiener (W), and Szeged (Sz) indices. Multiple regression analyses showed that MRI provided excellent results upon introduction of indicator parameters. Predictive ability of the proposed models was discussed using cross-validation parameters.     

Simulation of chemical metabolism for fate and hazard assessment. I. Approach for simulating metabolism

Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.     

Determination of the electron density in methyl (±)‐(1S,2S,3R)‐2‐methyl‐1,3‐diphenylcyclopropanecarboxylate using refinements with X‐ray scattering factors from wavefunction calculations of the whole molecule

The molecule of the title compound, C₁₈H₁₈O₂, is a substituted cyclopropane ring. The electron density in this molecule has been determined by refining single‐crystal X‐ray data using scattering factors derived from quantum mechanical calculations. Topological analysis of the electron densities in the three cyclopropane C—C bonds was carried out. The results show the effects of this substitution on these C—C bonds.     

Rotamer decomposition and protein dynamics: Efficiently analyzing dihedral populations from molecular dynamics

Molecular mechanics methods have matured into powerful methods to understand the dynamics and flexibility of macromolecules and especially proteins. As multinanosecond to microsecond length molecular dynamics (MD) simulations become commonplace, advanced analysis tools are required to generate scientifically useful information from large amounts of data. Some of the key degrees of freedom to understand protein flexibility and dynamics are the amino acid residue side chain dihedral angles. In this work, we present an easily automated way to summarize and understand the relevant dihedral populations. A tremendous reduction in complexity is achieved by describing dihedral timeseries in terms of histograms decomposed into Gaussians. Using the familiar and widely studied protein lysozyme, it is demonstrated that our approach captures essential properties of protein structure and dynamics. A simple classification scheme is proposed that indicates the rotational state population for each dihedral angle of interest and allows a decision if a given side chain or peptide backbone fragment remains rigid during the course of an MD simulation, adopts a converged distribution between conformational substates or has not reached convergence yet. © 2012 Wiley Periodicals, Inc.     

Determination of Drop Sizes in Annular Gas/Liquid Flows in Vertical and Horizontal Pipes

Measurement of drop sizes play vital role in applications dealing with gas/liquid flow mixtures. In the present work, drop sizes in vertical and horizontal pipe flows were determined using Malvern 2600HSD system that applies laser diffraction method. From the analysis of the experimental data obtained at two different pipe orientations, two separate expressions were developed to determine Sauter mean diameter, d ₃₂. Except for the 20 m/s superficial gas velocity in vertical flow case, a good agreement was found between the developed expressions and the experimental data.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.