A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas).

REACH regulation demands information about acute toxicity of chemicals towards fish and supports the use of QSAR models, provided compliance with OECD principles. Existing models present some drawbacks that may limit their regulatory application. In this study, a dataset of 908 chemicals was used to develop a QSAR model to predict the LC50 96 hours for the fathead minnow. Genetic algorithms combined with k nearest neighbour method were applied on the training set (726 chemicals) and resulted in a model based on six molecular descriptors. An automated assessment of the applicability domain (AD) was carried out by comparing the average distance of each molecule from the nearest neighbours with a fixed threshold. The model had good and balanced performance in internal and external validation (182 test molecules), at the expense of a percentage of molecules outside the AD. Principal Component Analysis showed apparent correlations between model descriptors and toxicity.

Impact of medium-distance pollution sources in a Galician suburban site (NW Iberian peninsula).

This work studies airborne quality in a geographical area that has not been investigated broadly: a suburban site nearby A Coruña (Galicia, NW Iberian Peninsula). In contrast to major Spanish cities, the site has Atlantic characteristics: rainy, scarce calm weather and infrequent prolonged sunny periods. The relationships between several gaseous pollutants (NO, NO2, NOx, CO, O3, PM10, PM2.5 and PM1) and their temporal trends (daily, monthly and seasonal) were evaluated. The aim was to unravel whether medium- and long-distance sources were impacting upon the site. Univariate studies focused on factorizing the pollutants according to a codifying factor (wind direction, hour of the day, season and month). Multivariate studies (Varimax-rotated factorial analysis) were done separately on both weekdays and weekends. The intensity of the daily maxima for NO, NO2, NOx and CO was lower during the weekends, with O3 behaving opposite. PM average values agreed with previous historical reports for a rural background station relatively close to the site and they decreased daily between 11:00 and 19:00 h, likely because of the marine breeze. With moderate wind speeds the pollutants were associated to medium-distance pollution sources, mainly the city of A Coruña and a combination of industrial pollution sources (a power plant, a solid waste incinerator and a regional airport).

Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna.

Quantitative structure-activity relationship (QSAR) models for predicting acute toxicity to Daphnia magna are often associated with poor performances, urging the need for improvement to meet REACH requirements. The aim of this study was to evaluate the accuracy, stability and reliability of a previously published QSAR model by means of further external validation and to optimize its performance by means of extension to new data as well as a consensus approach. The previously published model was validated with a large set of new molecules and then compared with ChemProp model, from which most of the validation data were taken. Results showed better performance of the proposed model in terms of accuracy and percentage of molecules outside the applicability domain. The model was re-calibrated on all the available data to confirm the efficacy of the similarity-based approach. The extended dataset was also used to develop a novel model based on the same similarity approach but using binary fingerprints to describe the chemical structures. The fingerprint-based model gave lower regression statistics, but also less unpredicted compounds. Eventually, consensus modelling was successfully used to enhance the accuracy of the predictions and to halve the percentage of molecules outside the applicability domain.

Correlation between the structural and optical properties of spontaneously formed GaN nanowires: a quantitative evaluation of the impact of nanowire coalescence.

We investigate the structural and optical properties of spontaneously formed GaN nanowires with different degrees of coalescence. This quantity is determined by an analysis of the cross-sectional area and perimeter of the nanowires obtained by plan-view scanning electron microscopy. X-ray diffraction experiments are used to measure the inhomogeneous strain in the nanowire ensembles as well as the orientational distribution of the nanowires. The comparison of the results obtained for GaN nanowire ensembles prepared on bare Si(111) and AlN buffered 6H-SiC(0001) reveals that the main source of the inhomogeneous strain is the random distortions caused by the coalescence of adjacent nanowires. The magnitude of the strain inhomogeneity induced by nanowire coalescence is found not to be determined solely by the coalescence degree, but also by the mutual misorientation of the coalesced nanowires. The linewidth of the donor-bound exciton transition in photoluminescence spectra does not exhibit a monotonic increase with the coalescence degree. In contrast, the comparison of the root mean square strain with the linewidth of the donor-bound exciton transition reveals a clear correlation: the higher the strain inhomogeneity, the larger the linewidth.

Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells.

Particulate matter (PM), a complex mix of chemical compounds, results to be associated with various health effects. However there is still lack of information on the impact of its different components. PM2.5 and PM1 samples, collected during the different seasons at an urban, rural and remote site, were chemically characterized and the biological effects induced on A549 cells were assessed. A Partial Least Square Discriminant Analysis has been performed to relate PM chemical composition to the toxic effects observed. Results show that PM-induced biological effects changed with the seasons and sites, and such variations may be explained by chemical constituents of PM, derived both from primary and secondary sources. The first-time here reported biological responses induced by PM from a remote site at high altitude were associated with the high concentrations of metals and secondary species typical of the free tropospheric aerosol, influenced by long range transports and aging.

Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods.

A recent approach based on self-organizing maps (SOMs) to extract patterns from three-way data, named MOLMAP, was applied in a four-seasons study on soil pollution and its results compared with three different conventional approaches: Parallel factor analysis (PARAFAC), matrix augmented principal components analysis (MA-PCA) and Procrustes rotation. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Zn, loss on ignition, pH and humidity). It was found that all techniques yielded highly similar results as the samples became organized in two major groups, each with a differentiated pollution pattern. This confirmed MOLMAP as a reliable option to handle environmental three-way datasets and to extract accurate pollution patterns.

In situ analysis of strain relaxation during catalyst-free nucleation and growth of GaN nanowires.

Strain relaxation mechanisms occurring during self-induced growth of nitride nanowires are investigated by in situ reflection high-energy electron diffraction and ex situ high-resolution transmission electron microscopy. Epitaxial GaN nanowires nucleate on an AlN buffer layer under highly nitrogen-rich conditions via the initial formation of coherently strained three-dimensional islands according to the Volmer-Weber growth mechanism. The epitaxial strain relief in these islands occurs by two different processes. Initially, strain is elastically relieved via several shape transitions. Subsequently, plastic relaxation takes place through the formation of a misfit dislocation at the GaN/AlN interface. At the same time, a final shape transition to fully relaxed nanowires occurs.

Self organizing maps for analysis of polycyclic aromatic hydrocarbons 3-way data from spilled oils.

In this paper, the application of a new method based on self-organizing maps (SOM; termed MOLMAP, molecular map of atom-level properties) to handle 3-way data generated in a monitoring environmental study is presented. The study comprised 50 polycyclic aromatic hydrocarbons (PAHs) analyzed in samples derived from the weathering of six oil products (four crude oils and two fuel oils) spilled under controlled conditions for about 4 months. MOLMAP yielded useful information on each mode of the data cube: weathering samples, spilled oil products, and PAHs. Thus, the different behaviors of the six oils were ascertained, along with their particular evolution on time, and their weathering patterns were studied in terms of the original PAHs. Thus, the two heaviest products (two fuel oils) were characterized by two neurons whose more relevant weights were associated to heavy PAHs, as C(1)-fluoranthene, C(2)-fluoranthene, benzo(ghi)perylene, and dibenz(ah)anthracene. The six spilled products were projected on different regions on both the MOLMAP-SOM and a subsequent principal components analysis (PCA) scatter plot, developed using the so-called MOLMAP-scores. Besides, it was possible to further differentiate between unweathered, or slightly weathered, samples and the most weathered ones. The more relevant PAHs characterizing those samples were assessed studying the weights of the neurons in which the samples got projected.

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications.

So far, similarity/diversity of objects has been widely studied in different research fields and a number of distance measures to estimate diversity between objects have been proposed. However, not much interest has been addressed to analysis of how diverse are configurations of objects in two different multivariate spaces. Since computerisation and automation nowadays lead to a large availability of information, it is apparent that a system could be described in different ways and, consequently, methods for comparison of the different viewpoints are required. These methods, for instance, may be usefully applied to Quantitative Structure-Activity Relationship (QSAR) studies. In this field, several thousands of molecular descriptors have been proposed in the literature and different selections of descriptors define different chemical spaces that need to be compared. Moreover, variable selection techniques such as Genetic Algorithms, Simulated Annealing, and Tabu Search are widely used to process available information in order to select optimal QSAR models. When more than one optimal model results, the problem arising is how to compare these models to find out whether they are really diverse or based on descriptors explaining almost the same information. In this paper, novel indices are proposed to measure similarity/diversity between pairs of data sets by the aid of the variable cross-correlation matrix.

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data Part 2. Variable reduction.

This paper proposes a new method for determining the subset of variables that reproduce as well as possible the main structural features of the complete data set. This method can be useful for pre-treatment of large data sets since it allows discarding variables that contain redundant information. Reducing the number of variables often allows one to better investigate data structure and obtain more stable results from multivariate modelling methods. The novel method is based on the recently proposed canonical measure of correlation (CMC index) between two sets of variables [R. Todeschini, V. Consonni, A. Manganaro, D. Ballabio, A. Mauri, Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications, Anal. Chim. Acta submitted for publication (2009)]. Following a stepwise procedure (backward elimination), each variable in turn is compared to all the other variables and the most correlated is definitively discarded. Finally, a key subset of variables being as orthogonal as possible are selected. The performance was evaluated on both simulated and real data sets. The effectiveness of the novel method is discussed by comparison with results of other well known methods for variable reduction, such as Jolliffe techniques, McCabe criteria, Krzanowski approach and its modification based on genetic algorithms, loadings of the first principal component, Key Set Factor Analysis (KSFA), Variable Inflation Factor (VIF), pairwise correlation approach, and K correlation analysis (KIF). The obtained results are consistent with those of the other considered methods; moreover, the advantage of the proposed CMC method is that calculation is very quick and can be easily implemented in any software application.

Characterization of DNA primary sequences by a new similarity/diversity measure based on the partial ordering.

The similarity/diversity measures play a fundamental role in library searching, virtual screening, and quantitative structure-activity relationship/quantitative structure-property relationship modeling as well as in genomics and proteomics. In this paper, a new similarity/diversity measure is proposed as a new approach for the analysis of sequential data, where useful information can be also obtained by the ordering relationships between the sequence elements. This methodology can be applied for evaluating molecular similarity/diversity, using sets of sequential descriptors, and for evaluating the similarity between spectra, sensor arrays, and other sequential data such as DNA and protein sequences. The new proposed distance (weighted standardized Hasse distance) is evaluated between pairs of Hasse matrices derived from the classical partial-ordering rules. It can be naturally standardized, thus allowing the interpretation of these distances as absolute values (e.g., percentage) and deriving simple similarity and correlation indices. A simple example is taken to highlight the behavior of the new similarity/diversity measure on DNA sequences taken from the first exons of the beta-globins for eight different species. Sensitivity analysis has been also performed, showing the high capability of this measure to take into account small modifications of the DNA sequences. Finally, a comparison with results obtained from the literature is given, together with a comparison with matrix invariants derived from the Hasse matrix.

Prediction of aromatic amines mutagenicity from theoretical molecular descriptors.

In the present research the mutagenicity data (Ames tests TA98 and TA100) for various aromatic and heteroaromatic amines, a data set extensively studied by other quantitative structure-activity relationship (QSAR)-authors, have been modeled by a wide set of theoretical molecular descriptors using linear multivariate regression (MLR) and genetic algorithm-variable subset selection (GA-VSS). The models have been calculated on a subset of compounds selected by a D-optimal experimental design. Moreover, they have been validated by both internal and external validation procedures showing satisfactory predictive performance. The models proposed here can be useful in predicting data and setting a testing priority for those compounds for which experimental data are not available or are not yet synthesized.

Classification of environmental pollutants for global mobility potential.

The environmental behaviour of global organic contaminants is known to be controlled by the physico-chemical properties of the compounds themselves. The principal component analysis of some physico-chemical properties, particularly relevant in determining mobility potential (vapour pressure, Henry's law constant, water solubility, K(OW), K(OA) and melting point) allows a multivariate approach to a ranking of organic pollutants according to their intrinsic tendency towards mobility, and the definition of four a priori mobility classes for screening purposes. Quantitative structure-property relationships (QSPRs) were used to predict missing values for octanol/air partition coefficients. Finally, a classification method employing theoretical molecular descriptors was used to assign studied chemicals to four mobility classes. The proposed approach assesses, directly and simply, a pollutant's inherent tendency towards mobility using only knowledge of the pollutant's molecular structure; the approach is particularly useful for a preliminary screening and the prioritisation of organic pollutants of emerging environmental concern.

Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors.

Soil sorption coefficients (K(OC)) of 185 non-ionic organic heterogeneous pesticides have been studied searching for quantitative structure-property relationships (QSPRs). The chemical description of pesticide structure has been made in terms of some molecular descriptors: count descriptors, topological indices, information indices, fragment-based descriptors and weighted holistic invariant molecular (WHIM) descriptors; these last are statistical indices describing size, shape, symmetry and atom distribution of molecules in the three-dimensional space. Three new topological indices derived from the electrotopological state indices of Kier and Hall were proposed. Multiple linear regression analysis was performed after previous selection of the descriptors mostly correlated to the response by Genetic Algorithms. The obtained results confirm the capability of the proposed approach to give predictive models for one of the most important partition properties, such as soil sorption coefficient (K(OC)).

Resolution of mixtures of three nonsteroidal anti-inflammatory drugs by fluorescence using partial least squares multivariate calibration with previous wavelength selection by Kohonen artificial neural networks.

A spectrofluorometric method for the quantitative determination of flufenamic, mefenamic and meclofenamic acids in mixtures has been developed by recording emission fluorescence spectra between 370 and 550 nm with an excitation wavelength of 352 nm. The excitation-emission spectra of these compounds are deeply overlapped which does not allow their direct determination without previous separation. The proposed method applies partial least squares (PLS) multivariate calibration to the resolution of this mixture using a set of wavelengths previously selected by Kohonen artificial neural networks (K-ANN). The linear calibration graphs used to construct the calibration matrix were selected in the ranges from 0.25 to 1.00 mug ml(-1) for flufenamic and meclofenamic acids, and from 1.00 to 4.00 mug ml(-1) for mefenamic acid. A cross-validation procedure was used to select the number of factors. The selected calibration model has been applied to the determination of these compounds in synthetic mixtures and pharmaceutical formulations.

Toward an in vitro test for the diagnosis of allergy to penicillins. Synthesis, characterization, and use of beta-lactam and beta-lactam metabolite poly-L-lysines which recognize human IgE antibodies.

Conjugates of poly-L-lysine (PLL) containing a penicillin or a penicilloyl residue were prepared and characterized by 1H NMR and by size-exclusion (SE) HPLC. These conjugates were used in a radio allergo sorbent tests (RAST) test for the determination of allergy toward beta-lactams. The chemiometric evaluation of the data indicates that allergy to amoxycillin is different from allergy to the other beta-lactams tested. Furthermore, careful chemical characterization of the conjugates appears to be crucial to obtain meaningful information from the RAST data.