Publications

Abstract The conformational preferences of cocaine and ecgonine methyl ester were determined through ab initio and density functional theory calculations. They share the same preferred orientation of the acetate group with a hydrogen bond between the amine and carbonyl groups, and s-cis conformation for the methoxyl group. The benzoyloxy group of cocaine defines a specific accessible conformational region. In solution the most stable conformers are stabilized by internal hydrogen bonds in contrast to the lesser stables, which are stabilized by solute/solvent interactions. Overall, these conformational features explain why ecgonine methyl ester is the principal metabolite of cocaine in a human environment.

Abstract The standard (p degrees = 0.1 MPa) molar enthalpy of formation of 4,5-dichloro-2-nitroaniline, in the gaseous phase, at T = 298.15 K, was derived from the combination of the values of the standard molar enthalpy of formation, in the crystalline phase, at T = 298.15 K, and the standard molar enthalpy of sublimation, at the same temperature. The standard molar enthalpy of formation, in the crystalline phase, at T = 298.15 K, was derived as -(99.7 +/- 1.6) kJ.mol(-1) from the standard massic energy of combustion, in oxygen, measured by rotating-bomb combustion calorimetry. The standard molar enthalpy of sublimation was calculated. (109.4 +/- 0.9) kJ.mol(-1) by the application of the Clausius-Clapeyron equation, to the vapour pressures measured at several temperatures by Knudsen effusion technique. The standard molar enthalpies of formation, in the gaseous phase, of the six dichloro-2-nitroaniline isomers and of the four dichloro-4-nitroaniline isomers were estimated by the Cox Scheme and by the Domalski and Hearing group additivity method and compared with the available experimental values. For the Domalski and Hearing group additivity method four new correction terms were derived.

Abstract Two new methods for inorganic pyrophosphate (PPi) quantification are described. They are based on the enzymatic conversion of PPi into ATP by firefly luciferase (Luc. E.C. 1.13.12.7) in the presence of dehydroluciferyl-adenylate (L-AMP) followed by the determination of ATP by one of two different procedures, either UV-monitored (260nm) ion-pair-HPLC (IP-HPLC) (method A) or luciferase-dependent bioluminescence in the presence Of its Substrate, firefly luciferin (D-LH(2)) (method B). These methods were subjected to optimization using experimental design methodologies to obtain optimum values for the selected factors: method A-incubation time (t(inc) = 15 min), inactivation time of the enzyme (t(inac) = 2 min), pH of the reaction mixture (pH 7.50) and the concentrations of L-AMP ([L-AMP] = 40 mu M) and luciferase ([Luc] = 0.1 mu M); method B-concentrations of L-AMP ([L-AMP] = 2 mu M), luciferase ([Luc] = 50nM) and luciferin ([LH(2)] = 30 mu M). Method Alias a linear response over the range of 0.1-20 mu M of PPi, with a limit of detection (LOD) of 0.5 mu M and a limit of quantitation (LOQ) of 1.8 mu M. Precision, expressed as relative standard deviation (R.S.D.), is 7.4% at 1 mu M PPi and 5.9% at 8 mu M PPi. Method B has a linear response over the range of 0.75-6.0 mu M of PPi, with LOD and LOQ of 0.624 and 2.23 mu M, respectively, and a R.S.D. of 5.1% at 2.5 mu M PPi and 4.9% at 5 mu M PPi. Under optimized conditions sensitive and robust methods can be obtained for the analysis of PPi impurities in commercial nucleotides and tripolyphosphate (P(3)).

Abstract There are many of pathogen bacteria species which very different susceptibility profile to different antibacterial drugs. There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Bacteria Pairs (DBPs) of affinity/non-affinity drugs with similar/dissimilar bacteria and represented it as a large network, which may be used to identify drugs that can act on bacteria. Computational chemistry prediction of the biological activity based on one-target Quantitative Structure-Activity Relationship (ot-QSAR) studies substantially increases the potentialities of this kind of networks avoiding time and resource consuming experiments. Unfortunately almost all ot-QSAR models predict the biological activity of drugs against only one bacterial species. Consequently, multi-tasking learning to predict drug's activity against different species with a single model (mt-QSAR) is a goal of major importance. These mt-QSARs offer a good opportunity to construct drug-drug similarity Complex Networks. Unfortunately, almost QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multi-bacteria QSAR classification model. The model correctly classifies 202 out of 241 active compounds (83.8%) and 169 out of 200 non-active cases (84.5%). Overall training predictability was 84.13% (371 out of 441 cases). The validation of the model was carried out by means of external predicting series, classifying the model 197 out of 221 (89.4%) cases. In order to show how the model functions in practice a virtual screening was carried out recognizing the model as active 86.7%, 520 out of 600 cases not used in training or predicting series. Outputs of this QSAR model were used as inputs to construct a network. The observed network has 1242 nodes (DBPs), 772,736 edges or DBPs with similar activity (sDBPs). The network predicted has 1031 nodes, 641,377 sDBPs. After edge-to-edge comparison, we have demonstrated that the predicted network is significantly similar to the observed one and both have distribution closer to exponential than to normal.

Abstract The most important limitation of antifungal QSAR models is that they predict the biological activity of drugs against only one fungal species. This is determined due the fact that most of the up-to-date reported molecular descriptors encode only information about the molecular structure. Consequently, predicting the probability with which a drug is active against different fungal species with a single unifying model is a goal of major importance. Herein, we use the Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model that predicts the antifungal activity of more than 280 drugs against 90 fungi species. Linear discriminant analysis (LDA) was used to classify drugs into two classes as active or non-active against the different tested fungal species whose data we processed. The model correctly classifies 12434 out of 12566 non-active compounds (98.95%) and 421 out of 468 active compounds (89.96%). Overall training predictability was 98.63%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 6216 out of 6277 non-active compounds and 215 out of 239 active compounds. Overall training predictability was 98.7%. The present is the first attempt to calculate, within a unifying framework, the probabilities of antifungal action of drugs against many different species based on spectral moment's analysis.

Abstract A set of four N-thenoylthiocarbamic-O-n-alkylesters, where alkylester = propylester, butylester, pentylester and hexylester were structurally characterized in solid state by single crystal X-ray diffractometry. The supramolecular structure for each compound is stabilised by N-H center dot center dot center dot O hydrogen bonds. For each compound gaseous phase ab initio geometry optimizations for several conformations were performed at the B3LYP/6-311++G(d,p) level of theory in order to evaluate and compare the calculated geometry with the experimental molecular crystal geometry as well as to evaluate the energetic difference between several alkyl conformations. The compounds were further analysed by FTIR spectroscopy and the experimental FTIR spectra were compared with the calculated ones at B3LYP/6-311++G(d,p) level of theory. The structural results for the set were further used in the interpretation of the odd and even effect found previously in their thermophysical properties.

Abstract The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular Structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.

Abstract The formation and microstructure of a novel microemulsion based on a salt-free catanionic surfactant have been examined by considering the hexadecyltrimethylammonium octylsulfonate (TASo)-decane-D(2)O system and using small-angle neutron scattering and self-diffusion NMR. With focus on the emulsification failure boundary, o/w discrete droplets have been observed and characterized for all of the studied microemulsion range. The evaluation of the experimental data was facilitated by using structure factors of a model system composed of charged particles interacting with a screened Coulomb potential. Furthermore, a more simplified model involving a charge regulation mechanism has been employed. Both approaches support the view that the droplets are mainly spherical, fairly monodisperse, and charged. The net charge of the surfactant film is a consequence of the partial dissociation of the short-chain counterpart, owing to its higher solubility. We have further quantified how the droplet charge varies with volume fraction and, from that dependence, explained the unusual phase behavior of the TASo-water system, a seldom found coexistence of two lamellar liquid-crystalline phases in a binary system. This coexistence is quantitatively modeled in terms of a fine balance between the attractive and repulsive colloidal forces acting within the system.

Abstract The standard (p(o) = 0.1 MPa) molar enthalpies of formation of 2,4,6-trichloronitrobenzene, 2,3,5,6-tetrachloronitrobenzene, and pentachloronitrobenzene, in the crystalline state, at T = 298.15 K, were derived from the standard massic energies of combustion, in oxygen, at T = 298.15 K, measured by rotating-bomb combustion calorimetry. The standard molar enthalpies of sublimation, at T = 298.15 K, of 2,4,6-trichloronitrobenzene and pentachloronitrobenzene, were determined from the dependence with the temperature of the vapour pressures, measured by the Knudsen mass-loss effusion method, whereas for 2,3,5,6-tetrachloronitrobenzene, the Calvet drop microcalori metric technique was used. [GRAPHICS] The experimental values are also compared with estimates based on G3(MP2)//B3LYP computations, which have also been extended to all the isomers of the trichloro- and tetrachloronitrobenzene that were not studied experimentally.