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A new series of galactosyl-derived polymers has been used for the preparation of microspheres. The strategy is based on the modification of the terminal carboxylic group L-PLA by coupling to a galactosyl antenna in the presence of the peptide coupling agents : DCC/HOBT . the degree of functionalisation varies between 60 and 70%, and antenna density between 1.74 and 2.78. In an effort to develop a new way of drug delivery, especially for polymeric antifungal molecules, we have incorporated amphotericin B (AmB) into biodegradable galactosylated poly(L-lactic acid) L-PLA . These drug carriers were prepared by solvent evaporation method using an oil/water (o/w) emulsion. The ratio of galactosylated microspheres was 7.14 mg for L-PLA (encapsulation rate 45% of mole). In our yeast model, drug release depend on three factors : i) presence of galactosylated antennae, ii) length of galactosyl antenna and iii) nature of the polymer . These novel functionalized microspheres could be required for the delivering of therapeutic agents according to their recognition to specific cells .

The obtaining of novel class of natural/synthetic polymer blends is described. The material based on common natural carbohydrates i.e. starch and carrageenan as well as synthetic, conducting polymer, polyaniline in dimethylsulfoxide is shown as a system with interesting rheological properties. The presented research is focused on topology of the thin layer obtained by slow evaporation of polymers solution and some rheological properties by means of mechanical spectra.

The self-aggregation in aqueous solution of a new neo-pentyl amide of the 3-β-amino derivative of cholic acid (Na-n-penC) has been investigated in aqueous solution by surface tension and steady state-fluorescence spectroscopy of pyrene (used as a probe). The nature of the agregates was determined by transmission electron microscopy (TEM) revealing that vesicles are formed. The structure of the compound in the solid state was resolved by X-ray spectroscopy. The synthesis of the compound is also given.

The interacting induced-dipoles polarization model, implemented in our program POLAR, is used for the calculation of the effective polarizability of the zeolitic bridged OH group, which results much higher than that of the free silanol group. A high polarizability is also calculated for the bridged OH group with a Si 4+ , in absence of Lewis-acid promotion of silanol by Al 3+. The crystal polarizability is estimated from the Clausius–Mossotti relationship. Siliceous zeolites are low-permittivity isolators. The interaction of a weak base with the zeolitic OH can be considered as a local bond. Only when cations are located in the zeolite micropore, next to tetrahedra that contain trivalent cations, are large electrostatic fields generated. They are short ranged, and the positive cation charges are compensated for by corresponding negative lattice charges. A method for the calculation of fractal surfaces of crystals is presented. The fractal dimension D of fragments of zeolites is calculated. Results compare well with reference calculations (GEPOL). The active site of Brønsted acid zeolites is modelled by sets of Al–OH–Si units, which form 2–12-membered rings. Topological indices for the different active-site models are calculated. The comparison between GEPOL and SURMO2 allows calculating the active-site indices. Most cavities show no fractal character, while for the 6–8-units rings D lies in the range 4.0–4.3. The 6-ring shows the maximum D; it is expected to be the most reactive.

Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate’s σ-stereoisomers combinatorial library. Two linear discriminant analysis models, using nonstochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the σ-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.953 and s = 0.238) and stochastic (R2 = 0.961 and s = 0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q2 = 0.935 (scv = 0.259) and q2 = 0.946 (scv = 0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.

There are many different kinds of pathogen bacteria species with very different susceptibility profile to different antibacterial drugs. One limitation of QSAR models are the biological activity of drugs against only one bacteria species. In previous paper we develop one unified Markov model to describe the biological activity of different drugs tested in the literature against some of the antimicrobial species. Consequently predicting the probability with which a drug is active against different bacteria species with a single unify model is a goal of the major importance. This work develops one unified Markov model to describe the biological activity of more than 70 drugs tested in the references against to 96 bacteria species. Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested bacteria species processed the data. The model correctly classifies 199 out of 237 active compounds (83.9%) and 168 out of 200 non-active compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carring out by means of external predicting series, classifying the model 202 out 243, 83.13% of compounds. In order to show how the model function in practice a virtual screening was carring out recognizing the model as active 84.5%, 480 out of 568 antibacterial compounds not used in training or predicting series. The present is an attempt to calculate withing a unify framework probabilities of antibacterial action of drugs against many different species.

In the present work, the pyrolysis of acetylene was studied under the conditions of vacuum carburizing of steel in a tubular °ow reactor. The pyrolysis temperature ranges from 650oC to 1050o C . The partial pressure of acetylene in the feed mixture was 10 mbar and 20 mbar respectively while the rest of the mixture consisted of nitrogen. The total pressure of the mixture was 1.6 bar. A kinetic mechanism which consists of 7 species and 9 reactions has been used in the commercial CFD code Fluent. Species transport and reaction model of Fluent was used in the simulations.The comparison of simulations and experimental results is presented in this paper.

In the present report it is presented the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behaviour is showed between the theoretical and experimental results. These results provided a useful tool that can be used in the identification of new tyrosinase inhibitor compounds.

QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragons descriptors and linear discriminant analysis (LDA) are presented here. A dataset of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active dataset was processed by k-means cluster analysis to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model (Eq. 3) in the training set. External validation processes to assess the robustness and predictive power of the obtained model was carried out. This external prediction set had an accuracy of 99.44%. After that, the developed were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidines series as new tyrosinase inhibitors. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed a good correspondence. These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitors compounds.