Phytochemical Analysis and Antiproliferative Activity of Ulex gallii Planch. (Fabaceae), a Medicinal Plant from Galicia (Spain).

The genus Ulex comprises thirteen accepted species of perennial shrubs in the family Fabaceae. In Galicia (Spain) many of these are considered spontaneous colonizing species, which are easy to establish and maintain. Among them, Ulex gallii Planch. is used in traditional medicine for the same anti-infective, hypotensive and diuretic purposes as Ulex europaeus L., which is the most studied species. Likewise, some studies have described the antitumoral properties of several species. However, there are few scientific studies that justify the use of Ulex gallii Planch. and nothing has been reported about its composition to date. In our study, the entire plant was extracted with methanol and the crude extract was subjected to liquid phase extraction with distinct solvents, yielding three fractions: hexane (H), dichloromethane (D) and methanol (M), which were subsequently fractionated. The dichloromethane (D5, D7 and D8) and methanol (M4) sub-fractions showed antiproliferative activity on A549 (lung cancer) and AGS (stomach cancer) cell lines, and caspase 3/7 activity assessment and DNA quantification were also performed. Targeted analysis via UHPLC-QToF, in combination with untargeted analysis via MS-Dial, MS-Finder and Global Natural Products Social Molecular Networking (GNPS), allowed us to tentatively identify different metabolites in these sub-fractions, mostly flavonoids, that might be involved in their antiproliferative activity.

Curcumin-Coumarin Hybrid Analogues as Multitarget Agents in Neurodegenerative Disorders.

Neurodegenerative diseases have a complex nature which highlights the need for multitarget ligands to address the complementary pathways involved in these diseases. Over the last decade, many innovative curcumin-based compounds have been designed and synthesized, searching for new derivatives having anti-amyloidogenic, inhibitory of tau formation, as well as anti-neuroinflammation, antioxidative, and AChE inhibitory activities. Regarding our experience studying 3-substituted coumarins with interesting properties for neurodegenerative diseases, our aim was to synthesize a new series of curcumin-coumarin hybrid analogues and evaluate their activity. Most of the 3-(7-phenyl-3,5-dioxohepta-1,6-dien-1-yl)coumarin derivatives 11-18 resulted in moderated inhibitors of hMAO isoforms and AChE and BuChE activity. Some of them are also capable of scavenger the free radical DPPH. Furthermore, compounds 14 and 16 showed neuroprotective activity against H2O2 in SH-SY5Y cell line. Nanoparticles formulation of these derivatives improved this property increasing the neuroprotective activity to the nanomolar range. Results suggest that by modulating the substitution pattern on both coumarin moiety and phenyl ring, ChE and MAO-targeted derivatives or derivatives with activity in cell-based phenotypic assays can be obtained.

Artificial Intelligence Applied to Flavonoid Data in Food Matrices.

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.

Hesperidin as a Neuroprotective Agent: A Review of Animal and Clinical Evidence.

Neuroprotection is the preservation of function and networks of neural tissues from damages caused by various agents, as well as neurodegenerative diseases such as Parkinson's, Alzheimer's, Huntington's diseases, and multiple sclerosis. Hesperidin, a flavanone glycoside, is a natural phenolic compound with a wide range of biological effects. Mounting evidence has demonstrated that hesperidin possesses inhibitory effect against development of neurodegenerative diseases. Our review discusses neuropharmacological mechanisms for preventive and therapeutic effects of hesperidin in neurodegenerative diseases. In addition, the review examines clinical evidence confirming its neuroprotective function. Various cellular and animal models specific to neurodegenerative diseases have been conducted to evaluate the underlying neuropharmacological mechanisms of hesperidin. Neuroprotective potential of this flavonoid is mediated by improvement of neural growth factors and endogenous antioxidant defense functions, diminishing neuro-inflammatory and apoptotic pathways. Despite the various preclinical studies on the role of hesperidin in the neurodegenerative diseases, less is known about its definite effect on humans. A limited number of clinical trials showed that hesperidin-enriched dietary supplements can significantly improve cerebral blood flow, cognition, and memory performance. Further clinical trials are also required for confirming neuroprotective efficacy of this natural flavonoid and evaluating its safety profile.

Computational Drug Target Screening through Protein Interaction Profiles.

The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 µM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3'-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 µM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3'-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 µM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety.

Design, synthesis and antibacterial study of new potent and selective coumarin-chalcone derivatives for the treatment of tenacibaculosis.

With the aim of finding new chemical entities selective for fish pathogens to avoid drug resistance in humans, a series of coumarin-chalcone hybrid compounds with different patterns of substitution were designed and synthesized. Their antibacterial activity was evaluated against important types of human bacteria strains (Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa) and against a fourteen strains of the marine pathogen Tenacibaculum maritimum, responsible for tenacibaculosis in fish, which is an important disease that causes great economical loss in the aquaculture industry. All the amino derivatives 5-12 presented high activity against different strains of T. maritimum, no activity against any of the three human pathogenic bacteria strains and no toxicity. Compounds 6, 7 and 11 were the most promising molecules. The most sensitive strains to these compounds were LL01 8.3.8 and LL01 8.3.1, being compound 11 up to 20 times more active than enrofloxacin. Therefore these scaffolds are good candidates for aquaculture treatments, avoiding possible drug resistance problems in humans.

Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors.

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRPs), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks.

One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug-drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.

New furan side tetracyclic allopsoralen derivatives: synthesis and photobiological evaluation.

Novel tetracyclic allopsoralen derivatives characterized by the condensation of a fourth cyclohexenylic (5-7) or benzenic (8-10) ring at the furan side and a methoxy (5 and 8), a hydroxy (6 and 9), or a dimethylaminopropoxy (7 and 10) side chain in the 10 position of the chromophore were prepared. Compounds 7 and 10 showed a strong photoantiproliferative activity, up to 3 orders of magnitude higher than that of the photochemotherapeutic drug 8-methoxypsoralen (8-MOP). The investigation into the mechanism of action demonstrated for 10 the capacity to intercalate between DNA base pairs in the ground state, to give rise to a covalent photoaddition upon UVA irradiation, and to inhibit polymerase chain reaction (PCR) in a sequence-specific manner. Conversely, compound 7 showed a limited capacity to form an intercalative complex and the lack of ability to photoadd to the macromolecule, thus revealing a novel and unusual behavior for an allopsoralen derivative.

Stochastic entropy QSAR for the in silico discovery of anticancer compounds: prediction, synthesis, and in vitro assay of new purine carbanucleosides.

A Markov model based QSAR is introduced for the rational selection of anticancer compounds. The model discriminates 90.3% of 226 structurally heterogeneous anticancer/non-anticancer compounds in training series. External validation series were used to validate the model; the 91.8% containing 85 compounds, not considered to fit the model, were correctly classified. The model developed is afterwards used in a simulation of a virtual search for anticancer compounds never considered either in training or in predicting series. The 87.7% of the 213 anticancer compounds used in this simulated search were correctly classified. The model also shows high values for specificity (0.89), sensitivity (0.91), and Mathews correlation coefficient (0.79). In addition, the present model compares better-to-similar with respect to other four models elsewhere reported if one takes into consideration 26 comparison parameters. Finally, we exemplify the use of the model in practice with the design of a new series of carbanucleosides. The compounds evaluated with the model were synthesized and experimentally assayed for their antitumor effects on the proliferation of murine leukemia cells (L1210/0) and human T-lymphocyte cells (CEM/0 and Molt4/C8). The more interesting activity was detected for the compound 5a with a predicted probability of 80.2% and IC(50) = 27.0, 27.2, and 29.4 microM, respectively, against the above-mentioned cellular lines. These values are comparable to those for the control compound Ara-A.

Design, synthesis, and vasorelaxant and platelet antiaggregatory activities of coumarin-resveratrol hybrids.

We have synthesized the coumarin-resveratrol hybrid 4 and its dimethoxy derivative 3 by a very direct synthetic route involving a Pechmann procedure. Compound 4 has also been synthesized by an alternative route (Perkin), which also allowed the synthesis of compounds 9-13. In addition, we have evaluated the potential vasorelaxant activity of the new compounds in endothelium-containing rat aorta rings pre-contracted with noradrenaline, as well as the inhibitory effects on platelet aggregation induced by thrombin in washed human platelets. The compounds reported here relaxed vascular smooth muscle and inhibited platelet aggregation with a profile similar to that of trans-resveratrol (t-RESV) and, in some cases, showed activity higher than that of the natural compound. This is the case for compound 13, which has a vasorelaxant activity that is twice as high as that of t-resveratrol and a platelet antiaggregant activity that is six times higher. These results suggest that these novel compounds may have potential as structural templates for the design and subsequent development of new vasodilatory and platelet antiaggregatory drugs.

In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of TOPS-MODE and 2D/3D connectivity indices. 2. Purine derivatives.

The TOPological Substructural MOlecular DEsign (TOPS-MODE) approach has been used to predict the anti-HIV activity in MT-4 assays (Estrada et al., 2002) of a diverse range of purine-based nucleosides. A database of 206 nucleosides has been selected from the literature and a theoretical virtual screening model has been developed. The model is able of discriminating between compounds that have anti-HIV activity and those that do not, with a good classification level of 85% in the training and 82.8% in the cross-validation series. On the basis of the information generated by the model, the correct classification of practically 80% of compounds from an external prediction set has been achieved using the theoretical model. Furthermore, the contribution of a range of molecular fragments to the pharmacological action has been calculated and this could provide a powerful tool in the design of nucleoside analogues that show activity against the HIV. Finally, a QSAR model has been developed that allows quantitative data to be obtained regarding the pharmacological potency shown by this type of compound.

Novel pyrone side tetracyclic psoralen derivatives: synthesis and photobiological evaluation.

This study reports the synthesis of tetrahydrobenzo- (4-6) and benzopsoralen (7-9) derivatives obtained by condensing the fourth ring to the pyrone side of the tricyclic psoralen moiety. The new compounds are characterized by having a methoxy, a hydroxy, or a dimethylaminopropoxy side chain inserted at position 8 of the psoralen chromophore. The evaluation of the photoantiproliferative activity on human tumor cell lines along with skin phototoxicity on guinea pigs revealed an interesting photobiological pattern for the dimethylaminopropoxy derivatives 6 and 9: they are in fact able to exert an antiproliferative effect up to 1 order of magnitude higher than that of the well-known drug 8-MOP, but they are devoid of skin phototoxicity. The ability of both 6 and 9 to photoadd to DNA is demonstrated by the isolation and characterization of the 4',5'-monoadducts. AM1 calculations were also performed to gain further insight into the molecular basis of their photobiological behavior.