Itaconic acid hybrids as potential anticancer agents

Molecular Diversity - In this paper, we report the synthesis of novel hybrids 2–14 based on itaconic acid and fluoroaniline, pyridine, indole and quinoline scaffolds. Itaconic acid is a...     

Discovery of ( ±)-3-(1 H -pyrazol-1-yl)-6,7-dihydro-5 H -[1,2,4]triazolo[3,4- b ][1,3,4] thiadiazine derivatives with promising in vitro anticoronavirus and antitumoral activity

A new series of (?±)-(3-(3,5-dimethyl-1H-pyrazol-1-yl)-6-phenyl-6,7-dihydro-5H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-7-yl)(phenyl)methanones were efficiently synthesized starting from 4-amino-5-hydrazinyl-4H-1,2,4-triazole-3-thiol 1, acetyl acetone 2, various aromatic and heterocyclic aldehydes 3 and phenacyl bromides 4. All the newly synthesized compounds were tested for their antiviral and antitumoral activity. It was shown that subtle structural variations on the phenyl moiety allowed to tune biological properties toward antiviral or antitumoral activity. Mode-of-action studies revealed that the antitumoral activity was due to inhibition of tubulin polymerization.

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Polyketide Synthase-Mediated O-Methyloxime Formation in the Biosynthesis of the Oximidine Anticancer Agents

Bacterial trans-acyltransferase polyketide synthases (trans-AT PKSs) are modular megaenzymes that employ unusual catalytic domains to assemble diverse bioactive natural products. One such PKS is responsible for the biosynthesis of the oximidine anticancer agents, oxime-substituted benzolactone enamides that inhibit vacuolar H⁺-ATPases. Here, we describe the identification of the oximidine gene cluster in Pseudomonas baetica and the characterization of four novel oximidine variants, including a structurally simpler intermediate that retains potent anticancer activity. Using a combination of in vivo, in vitro and computational approaches, we experimentally elucidate the oximidine biosynthetic pathway and reveal an unprecedented mechanism for O-methyloxime formation. We show that this process involves a specialized monooxygenase and methyltransferase domain and provide insight into their activity, mechanism and specificity. Our findings expand the catalytic capabilities of trans-AT PKSs and identify potential strategies for the production of novel oximidine analogues.     

In Vitro Antiplasmodial and Cytotoxic Activities of Compounds from the Roots of Eriosema montanum Baker f. (Fabaceae)

Malaria remains one of the leading causes of death in sub-Saharan Africa, ranked in the top three infectious diseases in the world. Plants of the Eriosema genus have been reported to be used for the treatment of this disease, but scientific evidence is still missing for some of them. In the present study, the in vitro antiplasmodial activity of the crude extract and compounds from Eriosema montanum Baker f. roots were tested against the 3D7 strain of Plasmodium falciparum and revealed using the SYBR Green, a DNA intercalating compound. The cytotoxicity effect of the compounds on a human cancer cell line (THP-1) was assessed to determine their selectivity index. It was found that the crude extract of the plant displayed a significant antiplasmodial activity with an IC₅₀ (µg/mL) = 17.68 ± 4.030 and a cytotoxic activity with a CC₅₀ (µg/mL) = 101.5 ± 12.6, corresponding to a selective antiplasmodial activity of 5.7. Bioactivity-guided isolation of the major compounds of the roots’ crude extract afforded seven compounds, including genistein, genistin and eucomic acid. Under our experimental conditions, using Artemisinin as a positive control, eucomic acid showed the best inhibitory activity against the P. falciparum 3D7, a well-known chloroquine-sensitive strain. The present results provide a referential basis to support the traditional use of Eriosema species in the treatment of malaria.     

The filtering approach to solvent peak suppression in MRS: a critical review

Suppressing the solvent peak is important in many applications of biomedical NMR spectroscopy in order to quantify the metabolites with a great accuracy. Among the postprocessing methods proposed in the literature, many deal with the concept of filtering. However, several proposals lack a theoretical perspective and some have not been explicitly applied to quantification problems. The present article is intended to bridge this gap: five methods are analyzed from a theoretical perspective. Subsequently the different methods are applied to the same set of data, and then the latter are quantified using the model fitting method AMARES. With our set, the scheme proposed by T. Sundin et al. (J. Magn. Reson. 139(2), 189-204 (1999)) proved to be the most reliable method.     

The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification

This study investigated the value of information from both magnetic resonance imaging and magnetic resonance spectroscopic imaging (MRSI) to automated discrimination of brain tumours. The influence of imaging intensities and metabolic data was tested by comparing the use of MR spectra from MRSI, MR imaging intensities, peak integration values obtained from the MR spectra and a combination of the latter two. Three classification techniques were objectively compared: linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel as linear techniques and LS-SVM with radial basis function kernel as a nonlinear technique. Classifiers were evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic (ROC) curve (AUC) was used as a global performance measure on test data. In general, all techniques obtained a high performance when using peak integration values with or without MR imaging intensities. For example for low- versus high-grade tumours, low- versus high-grade gliomas and gliomas versus meningiomas, the mean test AUC was higher than 0.91, 0.94, and 0.99, respectively, when both MR imaging intensities and peak integration values were used. The use of metabolic data from MRSI significantly improved automated classification of brain tumour types compared to the use of MR imaging intensities solely.     

Classification of brain tumours using short echo time 1H MR spectra

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas (n = 87), meningiomas (n = 57), metastases (n = 39), and astrocytomas grade II (n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.     

Preparation of Antiproliferative Terpene-Alkaloid Hybrids by Free Radical-Mediated Modification of ent-Kauranic Derivatives

A convenient strategy for molecular editing of available ent-kauranic natural scaffolds has been developed based on radical mediated C–C bond formation. Iodine atom transfer radical addition (ATRA) followed by rapid ionic elimination and radical azidoalkylation were investigated. Both reactions involve radical addition to the exo-methylenic double bond of the parent substrate. Easy transformations of the obtained adducts lead to extended diterpenes of broad structural diversity and artificial diterpene-alkaloid hybrids possessing lactam and pyrrolidine pharmacophores. The cytotoxicity of selected diterpenic derivatives was examined by in vitro testing on several tumor cell lines. The terpene-alkaloid hybrids containing N-heterocycles with unprecedented spiro-junction have shown relevant cytotoxicity and promising selectivity indexes. These results represent a solid basis for following research on the synthesis of such derivatives based on available natural product templates.     

Fast Removal of Residual Water in Proton Spectra

Quantification of metabolites in¹H spectra is difficult because of the presence of an unwanted water signal. Preprocessing, or removing the water contribution of a¹H spectrum, in the time domain is usually done using the state-space approach HSVD. HSVD removes the residual water and its side lobes, thereby reducing the baseline for the metabolites of interest and allowing subsequent data analysis using more sophisticated nonlinear least squares algorithms. However, the HSVD algorithm is computationally expensive because it estimates the signal subspace using the singular value decomposition (SVD). We show here that replacing the SVD by a low-rank revealing decomposition speeds up the computations without affecting the accuracy of the wanted parameter estimates.     

Frequency-domain method based on the singular value decomposition for frequency-selective NMR spectroscopy

In several applications of NMR spectroscopy the user is interested only in the components lying in a small frequency band of the spectrum. A frequency selective analysis deals precisely with this kind of NMR spectroscopy: parameter estimation of only those spectroscopic components that lie in a preselected frequency band of the NMR data spectrum, with as little interference as possible from the out-of-band components and in a computationally efficient way. In this paper we introduce a frequency-domain singular value decomposition (SVD)-based method for frequency selective spectroscopy that is computationally simple, statistically accurate, and which has a firm theoretical basis. To illustrate the good performance of the proposed method we present a number of numerical examples for both simulated and in vitro NMR data.