Photoredox catalysis leading to triazolo-quinoxalinones at room temperature: selectivity of the rate determining step.

The interest in the fusion product of quinoxalinone skeletons and 1,2,3-triazole units has greatly increased in recent years since they are known to be agonists of G-protein-coupled Niacin receptor 109A and inhibitors of the benzodiazepine and adenosine receptors. Here, we unveil the mechanism for the photoredox catalyzed synthesis of those scaffolds by means of DFT calculations. The calculations indicate that the rate determining step of this transformation is the attack of the in situ generated radical intermediate on the CN bond of the quinoxalinone species to form a new C-C bond. Predictive chemistry here reveals that the energy difference is so subtle, and gives the recipe of which substituents, sterically and electronically, can fit to perform the reaction at room temperature.

A predictive journey towards trans-thioamides/amides.

The cis-trans isomerization of (thio)amides was studied by DFT calculations to get the model for the higher preference for the cis conformation by guided predictive chemistry, suggesting how to select the alkyl/aryl substituents on the C/N atoms that lead to the trans isomer. Multilinear analysis, together with cross-validation analysis, helped to select the best fitting parameters to achieve the energy barriers of the cis to trans interconversion, as well as the relative stability between both isomers. Double experimental check led to the synthesis of the best trans candidate with sterically demanding t-butyl substituents, confirming the utility of predictive chemistry, bridging organic and computational chemistry.

Free-Radical Photopolymerization for Curing Products for Refinish Coatings Market.

Even though there are many photocurable compositions that are cured by cationic photopolymerization mechanisms, UV curing generally consists of the formation of cross-linking covalent bonds between a resin and monomers via a photoinitiated free radical polymerization reaction, obtaining a three-dimensional polymer network. One of its many applications is in the refinish coatings market, where putties, primers and clear coats can be cured faster and more efficiently than with traditional curing. All these products contain the same essential components, which are resin, monomers and photoinitiators, the latter being the source of free radicals. They may also include additives used to achieve a certain consistency, but always taking into account the avoidance of damage to the UV curing-for example, by removing light from the innermost layers. Surface curing also has its challenges since it can be easily inhibited by oxygen, although this can be solved by adding scavengers such as amines or thiols, able to react with the otherwise inactive peroxy radicals and continue the propagation of the polymerization reaction. In this review article, we cover a broad analysis from the organic point of view to the industrial applications of this line of research, with a wide current and future range of uses.

Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method.

The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Receiver Operating Characteristic (AU-ROC) values of 0.95 (leukemia prediction) and 0.80-0.90 (prostate). The preferential selected descriptors here are specific gene expressions, and this is potentially useful to point to possible key genes.

Investigation of Volatiles in Cork Samples Using Chromatographic Data and the Superposing Significant Interaction Rules (SSIR) Chemometric Tool.

This study describes a new chemometric tool for the identification of relevant volatile compounds in cork by untargeted headspace solid phase microextraction and gas chromatography mass spectrometry (HS-SPME/GC-MS) analysis. The production process in cork industries commonly includes a washing procedure based on water and temperature cycles in order to reduce off-flavors and decrease the amount of trichloroanisole (TCA) in cork samples. The treatment has been demonstrated to be effective for the designed purpose, but chemical changes in the volatile fraction of the cork sample are produced, which need to be further investigated through the chemometric examination of data obtained from the headspace. Ordinary principal component analysis (PCA) based on the numerical description provided by the chromatographic area of several target compounds was inconclusive. This led us to consider a new tool, which is presented here for the first time for an application in the chromatographic field. The superposing significant interaction rules (SSIR) method is a variable selector which directly analyses the raw internal data coming from the spectrophotometer software and, combined with PCA and discriminant analysis, has been able to separate a group of 56 cork samples into two groups: treated and non-treated. This procedure revealed the presence of two compounds, furfural and 5-methylfurfural, which are increased in the case of treated samples. These compounds explain the sweet notes found in the sensory evaluation of the treated corks. The model that is obtained is robust; the overall sensitivity and specificity are 96% and 100%, respectively. Furthermore, a leave-one-out cross-validation calculation revealed that all of the samples can be correctly classified one at a time if three or more PCA descriptors are considered.

Tryptophan-Containing Cyclic Decapeptides with Activity against Plant Pathogenic Bacteria.

A library of 66 cyclic decapeptides incorporating a Trp residue was synthesized on solid phase and screened against the phytopathogenic bacteria Pseudomonas syringae pv. syringae, Xanthomonas axonopodis pv. vesicatoria, and Erwinia amylovora. The hemolytic activity of these peptides was also evaluated. The results obtained were compared with those of a collection of Phe analogues previously reported. The analysis of the data showed that the presence of the Trp improved the antibacterial activity against these three pathogens. In particular, 40 to 46 Trp analogues displayed lower minimum inhibitory concentration (MIC) values than their corresponding Phe counterparts. Interestingly, 26 Trp-containing sequences exhibited MIC of 0.8 to 3.1 µM against X. axonopodis pv. vesicatoria, 21 peptides MIC of 1.6 to 6.2 µM against P. syringae pv. syringae and six peptides MIC of 6.2 to 12.5 µM against E. amylovora. Regarding the hemolysis, in general, Trp derivatives displayed a percentage of hemolysis comparable to that of their Phe analogues. Notably, 49 Trp-containing cyclic peptides showed a hemolysis ≤ 20% at 125 µM. The peptides with the best biological activity profile were c(LKKKLWKKLQ) (BPC086W) and c(LKKKKWLLKQ) (BPC108W), which displayed MIC values ranging from 0.8 to 12.5 µM and a hemolysis ≤ 8% at 125 µM. Therefore, it is evident that these Trp sequences constitute promising candidates for the development of new agents for use in plant protection.

Molecular Rearrangement of an Aza-Scorpiand Macrocycle Induced by pH: A Computational Study.

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method.

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.

Monitoring of sixteen fragrance allergens and two polycyclic musks in wastewater treatment plants by solid phase microextraction coupled to gas chromatography.

A methodology based on headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) determination was developed for the monitoring and evaluation of the removal efficiency of 16 common fragrance allergens and two polycyclic musks in wastewater treatment plants (WWTPs). An experimental design with a full factorial model was applied to evaluate the effects of the experimental parameters on the extraction (e.g., salt content, time and extraction temperature). After determining the optimum conditions (2.4 g NaCl, 45 min at 90 °C), an external calibration was performed and quality parameters of the proposed method were evaluated. Method detection limits in the range of 0.01-1.7 µg L(-1) were obtained. Satisfactory inter-day precision values between 4% and 23% (n=5) were obtained for most compounds. The method was applied to the monitoring of the target analytes in samples from two WWTPs. Seven target compounds were detected at the primary effluent of both plants at µg L(-1) levels. Limonene, linalool and eugenol were quantitatively eliminated during the secondary treatments of both WWTPs, while lilial, benzyl salicylate, galaxolide, and tonalide were still detected at the effluent waters.

Sequential discriminant classification of environments with different levels of exposure to tobacco smoke.

The use of biomarkers permits the detection of smoking having taken place in an environment. However, no single biomarker is able to differentiate clearly between different types of environments. Multivariate classification models have helped us to differentiate between outdoors, non-smoking indoors, well ventilated smoking indoors, and smoking environments without good air exchange. We found that the variables that enabled us to classify environments most accurately were indoor temperature, 2,5-dimethylfuran and ethyltoluene. A successful prediction rate of 86.5% was obtained by applying both direct fitting and cross validation discriminant (leave-one-out) analyses. Our results show that although a good air exchange ratio decreases the levels of volatile organic compounds in indoor air due to tobacco smoke, significant contamination still remains.

A probabilistic analysis about the concepts of difficulty and usefulness of a molecular ranking classification.

Discerning between the concepts of difficulty and usefulness of a molecular ranking classification is of significant importance in virtual design chemistry. Here, both concepts are viewed from the statistical and practical point of view according to the standard definitions of enrichment and statistical significance p-values. These parameters are useful not only to compare distinct rankings obtained for the same molecular database, but also in order to compare the ones established in distinct molecular sets from an objective point of view.

The connection between inverse and classical calibration.

Within the context of the simple classical linear calibration procedure (regression of y on x), here it is shown how a distinction between the distributions of the observed dependent variable (yobs) and the calculated (fitted) one (ycalc) leads to the following counterintuitive approach: in order to get the independent x values with lesser systematic deviations, do not identify as direct inputs in the classical calibration equation the new y observed ones (experimentally acquired), but instead the transformed ones by means of a regression towards the mean effect correction. It is shown how the conjunction of both steps, i.e., first the transformation of observed values and then the ulterior use in the classical calibration equation, corresponds to an operation totally equivalent to the direct implementation of the inverse calibration equation (regression of x on y in a single step). The reasoning given here explains in a simple manner why the inverse calibration numerically performs usually better for predictions of interpolated x values. Results are accompanied with the analysis of both theoretical and experimental data.

Softened electrostatic molecular potentials.

Electrostatic molecular potentials (EMPs) are studied from two points of view. First, a softened EMP (SEMP) approach is proposed, consisting in the substitution of a positive point charge as the entity with which an electronic density function (DF) interacts electrostatically to generate a classical EMP for a Gaussian charge distribution. Second, the performance of this SEMP approach under the Atomic Shell Approximation (ASA) is described and compared with classical EMP at the same ASA level. Several sample applications are presented to describe the general features of this new method of studying electrostatic interactions in molecules. The net result is a family of SEMPs that encompass EMPs as special cases but do not possess their infinite discontinuities. The features of SEMPs are quite similar to those of EMPs distant from nuclei, and the absence of infinity values makes them good candidates to be employed in molecular similarity calculations.

A headspace needle-trap method for the analysis of volatile organic compounds in whole blood.

Needle trap devices (NTDs) are a relatively new and promising tool for headspace (HS) analysis. In this study, a dynamic HS sampling procedure is evaluated for the determination of volatile organic compounds (VOCs) in whole blood samples. A full factorial design was used to evaluate the influence of the number of cycles and incubation time and it is demonstrated that the controlling factor in the process is the number of cycles. A mathematical model can be used to determine the most appropriate number of cycles required to adsorb a prefixed amount of VOCs present in the HS phase whenever quantitative adsorption is reached in each cycle. Matrix effect is of great importance when complex biological samples, such as blood, are analyzed. The evaluation of the salting out effect showed a significant improvement in the volatilization of VOCs to the HS in this type of matrices. Moreover, a 1:4 (blood:water) dilution is required to obtain quantitative recoveries of the target analytes when external calibration is used. The method developed gives detection limits in the 0.020-0.080µg L(-1) range (0.1-0.4µg L(-1) range for undiluted blood samples) with appropriate repeatability values (RSD<15% at high level and <23% at LOQ level). Figure of merits of the method can be improved by using a smaller phase ratio (i.e., an increase in the blood volume and a decrease in the HS volume), which lead to lower detection limits, better repeatability values and greater sensibility. Twenty-eight blood samples have been evaluated with the proposed method and the results agree with those indicated in other studies. Benzene was the only target compound that gave significant differences between blood levels detected in volunteer non-smokers and smokers.

Stereographic Projection of Density Functions (DF) and the Holographic Electronic Density Theorem (HEDT).

Mezey's holographic electronic density theorem is discussed from the point of view of stereographic projection techniques. Such a mathematical procedure is analyzed in depth from the point of view of first-order density functions; the procedure is then extended to any relevant quantum chemical function. This endeavor provides the background to construct a Holographic General Function Theorem (HGFT) for multivariate well-behaved functions. Stereographic projections, applied first as a way to obtain pictures of molecular quantum chemical functions, are shown to provide a flexible and original vantage point for visualizing, from any location and any chosen perspective, the form of any well-behaved function, irrespective of the number of variables involved. The pictographic possibilities of the HGFT are explored and exploited in several examples.

Odour-causing organic compounds in wastewater treatment plants: evaluation of headspace solid-phase microextraction as a concentration technique.

Odorous emissions from wastewater collection systems and treatment facilities affecting quality of life have given local populations reasons to complain for decades. In order to characterise the composition of such malodorous emissions, a method based on headspace solid-phase microextraction (HS-SPME) and gas chromatography coupled to mass spectrometry (GC-MS) has been developed to determine a list of compounds belonging to different chemical families, which have been previously described as potentially responsible for odour complaints, in wastewater matrices. Some parameters affecting the chromatographic behaviour of the target compounds were studied (e.g. splitless time). Experimental conditions affecting the extraction process (temperature, time and salt content) were evaluated by applying a factorial design at two levels. Using a DVB/CAR/PDMS fibre and the optimised HS-SPME conditions, calibration curves were constructed with detection limits in the range of 0.003-0.6 µg L(-1). Recovery values higher than 70% and relative standard deviation values between 5 and 16% (n=5) were obtained for all compounds and found to be satisfactory. In wastewater samples, a decrease in the concentration of the analysed compounds through the different treatments was observed. Most of the target analytes were found in influent samples while only octanal and carvone were detected in samples from the plant effluent.

Communications on quantum similarity, part 3: a geometric-quantum similarity molecular superposition algorithm.

This work describes a new procedure to obtain optimal molecular superposition based on quantum similarity (QS): the geometric-quantum similarity molecular superposition (GQSMS) algorithm. It has been inspired by the QS Aufbau principle, already described in a previous work, to build up coherently quantum similarity matrices (QSMs). The cornerstone of the present superposition technique relies upon the fact that quantum similarity integrals (QSIs), defined using a GTO basis set, depend on the squared intermolecular atomic distances. The resulting QSM structure, constructed under the GQSMS algorithm, becomes not only optimal in terms of its QSI elements but can also be arranged to produce a positive definite matrix global structure. Kruskal minimum spanning trees are also discussed as a device to order molecular sets described in turn by means of QSM. Besides the main subject of this work, focused on MS and QS, other practical considerations are also included in this study: essentially the use of elementary Jacobi rotations as QSM refinement tools and inward functions as QSM scaling methods.

Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses.

Antimicrobial cyclic decapeptides with anticancer activity.

Antimicrobial peptides have been considered as potential candidates for cancer therapy. We report here the cytotoxicity of a library of 66 antibacterial cyclodecapeptides on human carcinoma cell lines, and their effects on apoptosis [as assessed by cleavage of poly(ADP-ribose) polymerase (PARP)] and cell signaling proteins (p53 and ERK1/2) in cultured human cervical carcinoma cells. A design of experiments approach permitted to analyze the results of a subset of 16 peptides and define rules for high anticancer activity against MDA-MB-231 breast carcinoma cells. Eight peptides were identified with IC(50) values ranging from 18.5 to 57.5 µM against the five cell lines tested, being HeLa cells the most sensitive. Among these sequences, BPC88, BPC96, BPC98, and BPC194 displayed specificity and high cytotoxicity against HeLa cells (IC(50) of 22.5-38.5 µM), showed low hemolytic activity and low cytotoxicity to non-malignant fibroblasts, and were stable to proteases in human serum. Induction of apoptosis by these peptides was observed and the apoptotic effect of BPC88 and BPC96 caused a marked decrease on the activated form of ERK1/2 kinase and an induction of p53. We further showed that BPC96 at low doses synergized the cytotoxic effect of cisplatin. These findings suggest that cyclic decapeptides may represent novel anticancer agents providing a new strategy in cancer therapy.