Bulk Modification of Poly(lactide) (PLA) via Copolymerization with Poly(propylene glycol) Diglycidylether (PPGDGE).

Copolymers of l-lactide and poly(propylene glycol) diglycidyl ether (PPGDGE380) were synthesized by ring opening polymerization (ROP). Stannous octoate was used as the catalyst and 1-dodecanol as the initiator. The effect of the variables on the thermal properties of the copolymers was investigated by differential scanning calorimetry (DSC). Contact angle measurements were made in order to study the wettability of the synthesized copolymers. The copolymers differed widely in their physical characteristics, ranging from weak elastomers to tougher thermoplastics, according to the ratio of l-lactide and PPGDGE380. The results showed that the copolymers were more hydrophilic than neat Poly(lactide) (PLA) and the monomer ratio had a strong influence on the hydrophilic properties.

Synthesis and characterization of a HAp-based biomarker with controlled drug release for breast cancer.

A biocompatible hybrid porous polymer-ceramic material was synthesized to be used as a biomarker in the treatment of breast cancer. This device was equipped with the capacity to release medicaments locally in a controlled manner. The biomaterial was Hydroxyapatite(HAp)-based and had a controlled pore size and pore volume fraction. It was implemented externally using a sharp end and a pair of barbed rings placed opposite each other to prevent relative movement once implanted. The biomarker was impregnated with cis-diamine dichloride platinum (II) [Cl2-Pt-(NH3)2]; the rate of release was obtained using inductively coupled plasma atomic emission spectroscopy (ICP-AES), and release occurred over the course of three months. Different release profiles were obtained as a function of the pore volume fraction. The biomaterial was characterized using scanning electron microscopy (SEM) and Raman spectroscopy.

Determination of lead ion removal from a flowing electrolyte in the presence of a magnetic field using Raman spectroscopy.

PURPOSE: The authors report on the development of a new, noninvasive method to efficiently remove metal ions in aqueous solution flowing in a tube and to quantify the concentrations of those ions. Such a technique could be used to remove toxic ions in the interiors of arteries and veins in patients intoxicated by the ingestion of metal ions. METHODS: A magnetic field is applied to an aqueous electrolyte flowing in a specially designed rectangular cell in order to deflect the ion trajectories and concentrate them at one side of a cell. Once the ions are concentrated, they can be removed. Raman spectroscopy is used to promptly determine the concentration of the removed lead ions. RESULTS: It is possible to increase, on one side of the cell, the ion concentration by more than 80% with respect to the average concentration; the removed ions were taken from this high concentration region. This approach is a rapid, efficient, and noninvasive method for the removal of ions in aqueous solution. Raman spectroscopy was found to be a suitable technique to determine the amount of removed ions. CONCLUSIONS: The results indicate that the ion concentration can be increased more than 80% in a region where they can be removed. The increment in the ion concentration produced by the deflection due to the magnetic field, together with the use of Raman spectroscopy, allows for a rapid analysis of the removed ions without any previous preparation. The proposed method is a potentially useful method for metal ion separation of interest in the medical physics field.

Multidimensional drug design: simultaneous analysis of binding and relative efficacy profiles of N(6)-substituted-4'-thioadenosines A3 adenosine receptor agonists.

Desirability theory (DT) is a well-known multi-criteria decision-making approach. In this work, DT is employed as a prediction model (PM) interpretation tool to extract useful information on the desired trade-offs between binding and relative efficacy of N(6)-substituted-4'-thioadenosines A3 adenosine receptor (A3AR) agonists. At the same time, it was shown the usefulness of a parallel but independent approach providing a feedback on the reliability of the combination of properties predicted as a unique desirability value. The appliance of belief theory allowed the quantification of the reliability of the predicted desirability of a compound according to two inverse and independent but complementary prediction approaches. This information is proven to be useful as a ranking criterion in a ligand-based virtual screening study. The development of a linear PM of the A3AR agonists overall desirability allows finding significant clues based on simple molecular descriptors. The model suggests a relevant role of the type of substituent on the N(6) position of the adenine ring that in general contribute to reduce the flexibility and hydrophobicity of the lead compound. The mapping of the desirability function derived of the PM offers specific information such as the shape and optimal size of the N(6) substituent. The model herein developed allows a simultaneous analysis of both binding and relative efficacy profiles of A3AR agonists. The information retrieved guides the theoretical design and assembling of a combinatorial library suitable for filtering new N(6)-substituted-4'-thioadenosines A3AR agonist candidates with simultaneously improved binding and relative efficacy profiles. The utility of the desirability/belief-based proposed virtual screening strategy was deduced from our training set. Based on the overall results, it is possible to assert that the combined use of desirability and belief theories in computational medicinal chemistry research can aid the discovery of A3AR agonist candidates with favorable balance between binding and relative efficacy profiles.

Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides.

Cancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82 % of compounds in such a large and diverse dataset, with only 5 % of false inactives and 11 % of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86 % were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.

Prediction of telomerase inhibitory activity for acridinic derivatives based on chemical structure.

Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.

Synthesis and quantitative structure-antifungal activity relationships of clovane derivatives against Botrytis cinerea.

Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).

A TOPological Sub-structural Molecular Design (TOPS-MODE)-QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210).

Lately, Quantitative Structure-Activity Relationship (QSAR) studies have been afar used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines and data set of congeneric and non-congeneric compounds. Herein we report a QSAR study based on a TOPological Sub-structural Molecular Design (TOPS-MODE) approach, aiming at predicting the anticancer leukemia activity of a diverse data set of indolocarbazoles derivatives. Finally, several aspects of the structural activity relationships are discussed in terms of the contribution of different bonds to the anticancer activity, thereby making the relationship between structure and biological activity more transparent.

Variable selection methods in QSAR: an overview.

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

Applications of 2D descriptors in drug design: a DRAGON tale.

In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activity/toxicity prediction have been described in the literature, using molecular 0D, 1D, 2D and 3D descriptors. Also these descriptors have been implemented in available computational tools such as DRAGON, SYBYL and CODESSA for it easy use. However, many of them only have been used to explain a few prediction problems. This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse. Several topological molecular descriptors applications are described, ranging from simple topological indices to topological indices derived from matrices weighted with atomic and bond properties. Their advantages, limitations and its possibilities in drug design are also discussed.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds: species: rat; sex: male; route of administration: water.

In this work, Quantitative Structure-Activity Relationship (QSAR) modelling was used as a tool for predicting the carcinogenic potency of a set of 39 nitroso-compounds, which have been bioassayed in male rats by using the oral route of administration. The optimum QSAR model provided evidence of good fit and performance of predicitivity from training set. It was able to account for about 84% of the variance in the experimental activity and exhibited high values of the determination coefficients of cross validations, leave one out and bootstrapping (q(2)(LOO)=78.53 and q(2)(Boot)=74.97). Such a model was based on spectral moments weighted with Gasteiger-Marsilli atomic charges, polarizability and hydrophobicity, as well as with Abraham indexes, specifically the summation solute hydrogen bond basicity and the combined dipolarity/polarizability. This is the first study to have explored the possibility of combining Abraham solute descriptors with spectral moments. A reasonable interpretation of these molecular descriptors from a toxicological point of view was achieved by means of taking into account bond contributions. The set of relationships so derived revealed the importance of the length of the alkyl chains for determining carcinogenic potential of the chemicals analysed, and were able to explain the difference between mono-substituted and di-substituted nitrosoureas as well as to discriminate between isomeric structures with hydroxyl-alkyl and alkyl substituents in different positions. Moreover, they allowed the recognition of structural alerts in classical structures of two potent nitrosamines, consistent with their biotransformation. These results indicate that this new approach has the potential for improving carcinogenicity predictions based on the identification of structural alerts.

Halogenated derivatives QSAR model using spectral moments to predict haloacetic acids (HAA) mutagenicity.

The risk of the presence of haloacetic acids in drinking water as chlorination by-products and the shortage of experimental mutagenicity data for most of them requires a research work. This paper describes a QSAR model to predict direct mutagenicity for these chemicals. The model, able to describe more than 90% of the variance in the experimental activity, was developed with the use of the spectral moment descriptors. The model, using these descriptors with multiplicative effects provides better results than other linear descriptors models based on Geometrical, RDF, WHIM, eigenvalue-based indices, 2D-autocorrelation ones, and information descriptors, taking into account the statistical parameters of the model and the cross-validation results. The structural alerts and the mutagenicity-predicted values from the model output are in agreement with references from other authors. The mutagenicity predicted values for the three haloacetic acids, which have available experimental data (TCAA-Trichloroacetic acid, BDCAA-Bromodichloroacetic acid, and TBAA-Tribromoacetic acid), are reasonably close to their experimental values, specially for the latest two.

Quantitative structure-carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage.

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

QSAR modeling of the rodent carcinogenicity of nitrocompounds.

Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its conventional experimental test takes around three years to design, conduct, and interpret as well as the costs of hundreds of millions of dollars, millions of skilled personnel hours, and several animal lives. Both academia and private companies are actively trying to develop alternative methods, such as QSAR models. This paper reports a QSAR study for predicting carcinogenic potency of nitrocompounds bioassayed in female rats. Several different theoretical molecular descriptors, calculated only on the basis of knowledge of the molecular structure and an efficient variable selection procedure, such as Genetic Algorithm, led to models with satisfactory predictive ability. But the best-final QSAR model is based on the GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors capturing a reasonable interpretation. In fact, structural features such as molecular shape-linear, branched, cyclic, and polycyclic--and bond length are some of the key factors flagging the carcinogenicity of this set of nitrocompounds. This QSAR model, after removal of one identified nitrocompound outlier, is able to explain around 86% of the variance in the experimental activity and manifest good predictive ability as indicated by the higher q(2)s of cross- and external-validations, which demonstrate the practical value of the final QSAR model for screening and priority testing. This model can be applied to nitrochemicals different from the studied nitrocompounds (even those not yet synthesized) as it is based on theoretical molecular descriptors that might be easily and rapidly calculated.

Synthesis of new mannosyl, galactosyl and glucosyl theophylline nucleosides with potential activity as antagonists of adenosine receptors. DEMA-induced cyclization of glycosylideneiminouracils.

The synthesis of D-mannosyl, D-galactosyl and D-glucosyl theophylline nucleosides by diethoxymethyl acetate (DEMA)-induced cyclization of 4-amino-5-glycosylideneimino-1,3-dimethyluracil is reported. 8-Methyltheophylline derivatives of the same sugars were also prepared by Ac(2)O/H(+)-induced cyclization of their imine precursors. This approach has allowed beta-D-mannopyranosyl-, alpha-D-galactofuranosyl- and beta-D-glucofuranosyltheophylline nucleosides to be synthesized for the first time. The inhibition of specific binding at A(1), A(2A), A(2B) and A(3) adenosine receptors in the mannose derivatives is also reported.

Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?

In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of drug biological activity is advisable prior to synthesis and this can be achieved by employing predictive biological property methods. In this sense, Quantitative Structure-Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of QSAR applications to develop adenosine receptor (AR) antagonists is not common as for the case of the antibiotics and anticancer compounds for instance. The intention of this review is to summarize the present knowledge concerning computational predictions of new molecules as adenosine receptor antagonists.

New QSAR combined strategy for the design of A1 adenosine receptor agonists.

Combined discriminant and regression analysis was carried out on a series of 167 A1 adenosine receptor agonists to identify the best linear and nonlinear models for the design of new compounds with a better biological profile. On the basis of the best linear discriminant analysis and both linear and nonlinear Multi Layer Perceptron neural networks regression, we have designed and synthesized 14 carbonucleoside analogues of adenosine. Their biological activities were predicted and experimentally measured to demonstrate the capability of our model to avoid the prediction of false positives. A good agreement was found between the calculated and observed biological activity.

Radial Distribution Function descriptors for predicting affinity for vitamin D receptor.

The QSAR is an alternative method for the research of new and better Vitamin D analogues with affinity for the VDR receptor. This paper describes the results of applying the Radial Distribution Function (RDF descriptors) approach for predicting the VDR affinity of 38 vitamin D analogues. The model described 80% of the experimental variance, with a standard deviation of 0.35. Leave-one-out, bootstrapping and external set validation were carried out with the aim of evaluating the predictive power of the model. The values of their respective squared correlations coefficients were 0.72, 0.70 and 0.79. The RDF approach was compared with four other predictive models, but none of these could explain more than 71.0% of the variance with six variables in their respective models.

Quantitative structure-antifungal activity relationships of some benzohydrazides against Botrytis cinerea.

Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

Computational modeling tools for the design of potent antimalarial bisbenzamidines: overcoming the antimalarial potential of pentamidine.

Malaria is nowadays a worldwide and serious problem with a significant social, economic, and human cost, mainly in developing countries. In addition, the emergence and spread of resistance to existing antimalarial therapies deteriorate the global malaria situation, and lead thus to an urgent need toward the design and discovery of new antimalarial drugs. In this work, a QSAR predictive model based on GETAWAY descriptors was developed which is able to explain with, only three variables, more than 77% of the variance in antimalarial potency and displays a good internal predictive ability (of 73.3% and 72.9% from leave-one-out cross-validation and bootstrapping analyses, respectively). The performance of the proposed model was judged against other five methodologies providing evidence of the superiority of GETAWAY descriptors in predicting the antimalarial potency of the bisbenzamidine family. Moreover, a desirability analysis based on the final QSAR model showed that to be a useful way of selecting the predictive variable level necessary to obtain potent bisbenzamidines. From the proposed model it is also possible to infer that elevated high atomic masses/polarizabilities/van der Waals volumes could play a negative/positive/positive role in the molecular interactions responsible for the desired drug conformation, which is required for the optimal binding to the macromolecular target. The results obtained point out that our final QSAR model is statistically significant and robust as well as possessing a high predictive effectiveness. Thus, the model provides a feasible and practical tool for looking for new and potent antimalarial bisbenzamidines.