Phase II study of eribulin in combination with gemcitabine for the treatment of patients with locally advanced or metastatic triple negative breast cancer (ERIGE trial). Clinical and pharmacogenetic results on behalf of the Gruppo Oncologico Italiano di Ricerca Clinica (GOIRC).

BACKGROUND: The combination of a microtubule inhibitor (eribulin) with a nucleoside analog (gemcitabine) may synergistically induce tumor cell death, particularly in triple negative breast cancer (TNBC) characterized by high cell proliferation, aggressive behavior, and chemo-resistance. PATIENTS AND METHODS: This is an open-label, multicenter phase II study evaluating the combination of eribulin (0.88 mg/m2) plus gemcitabine (1000 mg/m2) on days 1 and 8 of a 21-day cycle as either first- or second-line treatment of locally advanced or metastatic TNBC. The primary endpoint was the objective response for evaluable patients. A prospective, molecular correlative study was carried out to assess the role of germinal BRCA pathogenic variants and single nucleotide polymorphisms (SNPs) in predicting efficacy and toxicity of the combination regimen. RESULTS: From July 2013 to September 2016, 83 evaluable patients were enrolled. They received a median number of six cycles of treatment. An overall response rate (ORR) of 37.3% (31 patients) was observed, with a complete response rate of 2.4% and a partial response rate of 34.9%; the clinical benefit rate was 48.8%. With a median follow-up of 28.8 months, the median response duration was 6.6 months, the median progression-free survival (PFS) was 5.1 months, and the median overall survival (OS) was 14.5 months. The most common grade 3-4 adverse events were aminotransferase elevation (in 25% of the patients) and neutropenia (in 23.8%). Women with BRCA1/2 pathogenic variants were associated with worse ORR, PFS, and OS than BRCA1/2 wild-type carriers. CYP3A4 and FGD4 SNPs were associated with increased risk of liver toxicity. Three different SNPs in CDA∗2, RRM1, and CYP2C8 genes were significantly associated with poorer OS. CONCLUSIONS: The combination of eribulin and gemcitabine showed promising activity and a moderate toxicity profile in metastatic TNBC. BRCA status and pharmacogenetics tests may help identify patients with high probability of response with negligible toxicity. EUDRACT NUMBER: 2012-003505-10.

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.

Ongoing outbreak of visceral leishmaniasis in Bologna Province, Italy, November 2012 to May 2013.

An increased number of autochthonous visceral leishmaniasis (VL) cases has recently been reported in Bologna Province in northern Italy. Over six months from November 2012 to May 2013, 14 cases occurred, whereas the average number of cases per year was 2.6 (range: 0-8) in 2008 to 2012. VL was diagnosed in a median of 40 days (range: 15-120) from disease onset. This delay in diagnosis shows the need for heightened awareness of clinicians for autochthonous VL in Europe.

Chemometric analysis of gas chromatography with flame ionisation detection chromatograms: a novel method for classification of petroleum products.

Most oil characterisation procedures are time consuming, labour intensive and utilise only part of the acquired chemical information. Oil spill fingerprinting with multivariate data processing represents a fast and objective evaluation procedure, where the entire chromatographic profile is used. Methods for oil classification should be robust towards changes imposed on the spill fingerprint by short-term weathering, i.e. dissolution and evaporation processes in the hours following a spill. We propose a methodology for the classification of petroleum products. The method consists of: chemical analysis; data clean-up by baseline removal, retention time alignment and normalisation; recognition of oil type by classification followed by initial source characterisation. A classification model based on principal components and quadratic discrimination robust towards the effect of short-term weathering was established. The method was tested successfully on real spill and source samples.

Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits.

Assessing the sensitivity of different species to chemicals is one of the key points in predicting the effects of toxic compounds in the environment. Trait-based predicting methods have proved to be extremely efficient for assessing the sensitivity of macroinvertebrates toward compounds with non specific toxicity (narcotics). Nevertheless, predicting the sensitivity of organisms toward compounds with specific toxicity is much more complex, since it depends on the mode of action of the chemical. The aim of this work was to predict the sensitivity of several freshwater macroinvertebrates toward three classes of plant protection products: organophosphates, carbamates and pyrethroids. Two databases were built: one with sensitivity data (retrieved, evaluated and selected from the U.S. Environmental Protection Agency ECOTOX database) and the other with biological traits. Aside from the "traditional" traits usually considered in ecological analysis (i.e. body size, respiration technique, feeding habits, etc.), multivariate analysis was used to relate the sensitivity of organisms to some other characteristics which may be involved in the process of intoxication. Results confirmed that, besides traditional biological traits, related to uptake capability (e.g. body size and body shape) some traits more related to particular metabolic characteristics or patterns have a good predictive capacity on the sensitivity to these kinds of toxic substances. For example, behavioral complexity, assumed as an indicator of nervous system complexity, proved to be an important predictor of sensitivity towards these compounds. These results confirm the need for more complex traits to predict effects of highly specific substances. One key point for achieving a complete mechanistic understanding of the process is the choice of traits, whose role in the discrimination of sensitivity should be clearly interpretable, and not only statistically significant.

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.

Total ranking models by the genetic algorithm variable subset selection (GA-VSS) approach for environmental priority settings.

Total order ranking (TOR) strategies, which are mathematically based on elementary methods of discrete mathematics, seem to be attractive and simple tools for performing data analysis. Moreover order-ranking strategies seem to be a very useful tool not only to perform data exploration but also to develop order ranking models, a possible alternative to conventional quantitative structure-activity relationship (QSAR) methods. In fact, when data material is characterised by uncertainties, order methods can be used as alternative to statistical methods such as multilinear regression (MLR), because they do not require specific functional relationships between the independent and dependent variables (responses). A ranking model is a relationship between a set of dependent attributes, experimentally investigated, and a set of independent attributes, i.e. model attributes, which are calculated attributes. As in regression and classification models, the variable selection model is one of the main steps in finding predictive models. In this work the genetic algorithm-variable subset selection (GA-VSS) approach is proposed as the variable selection method for searching for the best ranking models within a wide set of variables. The models based on the selected subsets of variables are compared with the experimental ranking and evaluated by the Spearman's rank index. A case study application is presented on a TOR model developed for polychlorinated biphenyl (PCB) compounds, which have been analysed according to some of their physicochemical properties which play an important role in their environmental impact.

Study on anaerobic and aerobic degradation of different non-ionic surfactants.

Six alcohol ethoxylates (C5E2, C6E4, C7E4, C8E2, C8E4, C10E4) and two fatty acid esters were tested at lab-scale for degradation in anaerobic and aerobic conditions and oxygen uptake rate (OUR). Anaerobic removal of C5E2, C6E4 and C7E4 improved with increasing number of ethoxy groups (E) and decreasing length of the alkyl chain (C). Their aerobic removal was also great but lower than the anaerobic values. C8E2, C8E4 and C10E4 were adsorbed on sludge but not degraded in anaerobic conditions, while they were efficiently removed under aerobiosis. The fatty acid esters were removed to a level between the two alcohol ethoxylates groups in both anaerobiosis and aerobiosis. The measured OUR confirmed the different behaviours of the three groups of compounds.

Application of the Kohonen artificial neural network in the identification of proteinaceous binders in samples of panel painting using gas chromatography-mass spectrometry.

Historically, three types of proteinaceous matter--casein, egg and animal glue--were used as binders for pigments or as adhesives in easel and wall painting. The relative percentage content of alanine, glycine, valine, leucine, isoleucine, serine, tyrosine, phenylalanine, aspartic acid, glutamic acid, lysine, methionine, proline and hydroxyproline, as determined by GC-MS, is used for binder identification. In this paper we analyse the viability of a multivariate modelling using Kohonen's neural network to characterise the wood adhesive in 16 old samples from Italian panel paintings of the 12-16th centuries. As a training set we use the amino acid composition of 141 samples contributed by the Opificio delle Pietre Dure of Florence (Cultural Heritage Ministry, Italy). Of the 141 samples, 113 were used to train the Kohonen neural network and the remaining 28 as the evaluation set. A specificity and sensitivity of 100% was achieved in training and 92-100% in prediction depending on the assignation criteria employed. The neural network thus trained and evaluated was applied to the old samples, achieving identification of all of them. In addition, the map obtained for each amino acid provides relevant information as to its importance in the characterisation of the sample.

QSAR and chemometric approaches for setting water quality objectives for dangerous chemicals.

In order to evaluate environmentally safe levels of dangerous chemicals, there is the need for a set of toxicological data on organisms representative of the ecosystems, which is often unavailable or inadequate. In this article, a predictive approach was applied to a set of 125 chemicals (derived from the European priority list in compliance with Directive 76/464/EEC), for which water quality objectives were available. Toxicological data on organisms representative of the aquatic environment (algae, Daphnia, and fish) were taken from the literature or predicted by means of quantitative structure--activity relationships. This provided toxicological data on all three organisms for 97 of 125 chemicals and on at least two organisms (Daphnia and fish) for the whole data set. Principal Component Analysis was applied in order to perform an a priori classification of chemicals based on toxicity data. Then several classification models, based on traditional and nontraditional molecular descriptors, were applied. Classification models gave results in agreement with the a priori classification as well as with the original water quality objectives classification. The behavior of some outliers was explained. The approach described appears to be a useful tool for the preliminary classification of chemicals that are dangerous to the aquatic environment for which toxicological data are inadequate.

Traditional versus WHIM molecular descriptors in QSAR approaches applied to fish toxicity studies.

Quantitative structure-activity relationships (QSAR) were performed on nine congenetic aromatic hydrocarbons. Acute response was evaluated in freshwater fish species. QSAR were built by Hansch's approaches and weighted holistic invariant molecular (WHIM) indices. The prediction power of QSAR from both approaches was evaluated. Single regression analysis derivated by Hansch's approach seem suitable for non-polar compounds. However, for all species, it has not a high predictive power (Q2(LOO)) of the biological activity from only K(ow) as molecular descriptor. Multiple regression analysis obtained from WHIM descriptors showed Q2(LOO) higher than 80%, indicating that molecular descriptors have a prediction power greater than K(ow).

QSAR approach for the selection of congeneric compounds with a similar toxicological mode of action.

The selection of compounds with a similar toxicological mode of action is a key problem in the study of chemical mixtures. In this paper, an approach for the selection of chemicals with similar mode of action, based on the analysis of structural similarities by means of QSAR and chemometric methods, is described. As a first step, a complete representation of chemical structures for examined chemicals (phenylureas and triazines) by different sets of molecular descriptors allows a preliminary exploration of similarity using multi-dimensional scaling (MDS). The use of genetic algorithm (GA) to select the most relevant molecular descriptors in modeling toxicity data makes it possible to develop predictive toxicity models. The final step is a similarity analysis, based again on MDS, using selected molecular descriptors, really relevant in describing the toxicological effect.

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.

Hydroxylamine-induced cleavage of the asparaginyl-glycine motif in the production of recombinant proteins: the case of insulin-like growth factor I.

Hydroxylamine-induced cleavage at the asparaginyl-glycine dipeptide site inserted between the two moieties of recombinant fusion proteins has been used at both the analytical and the preparative scale to obtain the mature protein. In this study a model protein containing a fusion precursor of insulin-like growth factor I was used to investigate the influence of the operating conditions on the cleavage reaction and the formation of undesired side products such as hydroxamate and deamidated analogs. Moreover, the stability of the cleavage site toward deamidation was examined and a chemometric study performed to define the effect of the reaction conditions on the cleavage yield and on the formation of side products.

MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D QSAR study in a series of steroids.

The recently proposed WHIM (Weighted Holistic Invariant Molecular) approach [Todeschini, R., Lasagni, M. and Marengo, E., J. Chemometrics, 8 (1994) 263] has been applied to molecular surfaces to derive new 3D theoretical descriptors, called MS-WHIM. To test their reliability, a 3D QSAR study has been performed on a series of steroids, comparing the MS-WHIM description to both the original WHIM indices and CoMFA fields. The analysis of the statistical models obtained shows that MS-WHIM descriptors provide meaningful quantitative structure-activity correlations. Thus, the results obtained agree well with those achieved using CoMFA fields. The concise number of indices, the ease of their calculation and their invariance to the coordinate system make MS-WHIM an attractive tool for 3D QSAR studies.