Molecular modeling as a promising tool to study dendrimer prodrugs delivery

Molecular modeling methodologies were applied to perform preliminary studies concerning the release of active agents from potentially antichagasic and antileishmanial dendrimer prodrugs. The dendrimer was designed having myo-inositol as a core, L-malic acid as a spacer group, and hydroxymethylnitrofurazone (NFOH), 3-hydroxyflavone or quercetin, as active compounds. Each dendrimer presented a particular behavior concerning to the following investigated properties: spatial hindrance, map of electrostatic potential (MEP), and the lowest unoccupied molecular orbital energy (E(LUMO)). Additionally, the findings suggested that the carbonyl group next to the active agent seems to be the most promising ester breaking point. (C) 2009 Elsevier B.V. All rights reserved.

fac-/mer-[RuCl(3)(NO)(P-N)] (P-N = [o-(N,N-dimethylamino)phenyl]diphenylphosphine): Synthesis, characterization and DFT calculations

Complex fac-[RuCl(3)(NO)(P-N)] (1) was synthesized from the reaction of [RuCl(3)(H(2)O)(2)(NO)] and the P-N ligand, o-[(N,N-dimethylamino)phenyl]diphenylphosphine) in refluxing methanol solution, while complex mer,trans-[RuCl(3)(NO)(P-N)] (2) was obtained by photochemical isomerization of (1) in dichloromethane solution. The third possible isomer mer, cis-[RuCl(3)(NO)(P-N)] (3) was never observed in direct synthesis as well as in photo-or thermal-isomerization reactions. When refluxing a methanol solution of complex (2) a thermally induced isomerization occurs and complex (1) is regenerated. The complexes were characterized by NMR ((31)P{(1)H}, (15)N{1H} and 1H), cyclic voltammetry, FTIR, UV-Vis, elemental analysis and X-ray diffraction structure determination. The (31)P{(1)H} NMR revealed the presence of singlet at 35.6 for (1) and 28.3 ppm for (2). The (1)H NMR spectrum for (1) presented two singlets for the methyl hydrogens at 3.81 and 3.13 ppm, while for (2) was observed only one singlet at 3.29 ppm. FTIR Ru-NO stretching in KBr pellets or CH(2)Cl(2) solution presented 1866 and 1872 cm(-1) for (1) and 1841 and 1860 cm(-1) for (2). Electrochemical analysis revealed a irreversible reduction attributed to Ru(II)-NO(+) -> Ru(II)-NO(0) at -0.81 V and -0.62 V, for (1) and (2), respectively; the process Ru(II) -> Ru(III), as expected, is only observed around 2.0 V, for both complexes. Studies were conducted using (15)NO and both complexes were isolated with (15)N-enriched NO. Upon irradiation, the complex fac-[RuCl(3)(NO)(P-N)] (1) does not exchange (14)NO by (15)NO, while complex mer, trans-[RuCl(3)(NO)(P-N)] (2) does. Complex mer, trans-[RuCl(3)((15)NO)(P-N)] (2`) was obtained by direct reaction of mer, trans-[RuCl(3)(NO)(P-N)] (2) with (15)NO and the complex fac-[RuCl(3)((15)NO)(P-N)] (1`) was obtained by thermal-isomerization of mer, trans-[RuCl(3)((15)NO)(P-N)] (2`). DFT calculation on isomer energies, electronic spectra and electronic configuration were done. For complex (1) the HOMO orbital is essentially Ru (46.6%) and Cl (42.5%), for (2) Ru (57.4%) and Cl (39.0%) while LUMO orbital for (1) is based on NO (52.9%) and is less extent on Ru (38.4%), for (2) NO (58.2%) and Ru (31.5%). (C) 2011 Elsevier B. V. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

A quantum chemical study on a set of non-imidazole H(3) antihistamine molecules

Molecular orbital calculations were carried out on a set of 28 non-imidazole H(3) antihistamine compounds using the Hartree-Fock method in order to investigate the possible relationships between electronic structural properties and binding affinity for H3 receptors (pK(i)). It was observed that the frontier effective-for-reaction molecular orbital (FERMO) energies were better correlated with pK(i) values than highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values. Exploratory data analysis through hierarchical cluster (HCA) and principal component analysis (PCA) showed a separation of the compounds in two sets, one grouping the molecules with high pK(i) values, the other gathering low pK(i) value compounds. This separation was obtained with the use of the following descriptors: FERMO energies (epsilon(FERMO)), charges derived from the electrostatic potential on the nitrogen atom (N(1)), electronic density indexes for FERMO on the N(1) atom (Sigma((FERMO))c(i)(2)). and electrophilicity (omega`). These electronic descriptors were used to construct a quantitative structure-activity relationship (QSAR) model through the partial least-squares (PLS) method with three principal components. This model generated Q(2) = 0.88 and R(2) = 0.927 values obtained from a training set and external validation of 23 and 5 molecules, respectively. After the analysis of the PLS regression equation and the values for the selected electronic descriptors, it is suggested that high values of FERMO energies and of Sigma((FERMO))c(i)(2), together with low values of electrophilicity and pronounced negative charges on N(1) appear as desirable properties for the conception of new molecules which might have high binding affinity. 2010 Elsevier Inc. All rights reserved.