Self-organizing maps and VolSurf approach to predict aldose reductase inhibition by flavonoid compounds

Aldose Reductase (AR) is the polyol pathway key enzyme which converts glucose to sorbitol. High glucose availability in insulin resistant tissues in diabetes leads into an accumulation of sorbitol, which has been associated with typical chronic complications of this disease, such as neuropathy, nephropathy and retinopathy. In this study, 71 flavonoids AR inhibitors were subjected to two methods of SAR to verify crucial substituents. The first method used the PCA (Principal Component Analysis) to elucidate physical and chemical characteristics in the molecules that would be essential for the activity, employing VolSurf descriptors. The rate obtained explained 53% of the system total variance and revealed that a hydrophobic-hydrophilic balance in the molecules is required, since very polar or nonpolar substituents decrease the activity. Artificial Neural Networks (ANNs) was also employed to determine key substituents by evaluating substitution patterns, using NMR data. This study had a high success rate (85% accuracy in the training set and 88% accuracy in the test set) and showed polihydroxilations are essential for high activity and methoxylations and glicosilations primarily at positions C7, C3' and C4' decrease the activity.CNPqFAPES

3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents

Tuberculosis (TB) is the primary cause of mortality among infectious diseases. Mycobacterium tuberculosis monophosphate kinase (TMPKmt) is essential to DNA replication. Thus, this enzyme represents a promising target for developing new drugs against TB. In the present study, the receptor-independent, RI, 4D-QSAR method has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 81 thymidine analogues, and two corresponding subsets, reported as inhibitors of TMPKmt. The resulting optimized models are not only statistically significant with r (2) ranging from 0.83 to 0.92 and q (2) from 0.78 to 0.88, but also are robustly predictive based on test set predictions. The most and the least potent inhibitors in their respective postulated active conformations, derived from each of the models, were docked in the active site of the TMPKmt crystal structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. Moreover, the QSAR models provide insights regarding a probable mechanism of action of the analogues.CAPES Foundationfederal scientific agency of BrazilNational Institutes of Health (NIH)[1 R21 GM075775

Rational Design and 3D-Pharmacophore Mapping of 5 `-Thiourea-Substituted alpha-Thymidine Analogues as Mycobacterial TMPK Inhibitors

Thymidine monophosphate kinase (TMPK) has emerged as an attractive target for developing inhibitors of Mycobacterium tuberculosis growth. In this study the receptor-independent (RI) 4D-QSAR formalism has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 5`-thiourea-substituted alpha-thymidine inhibitors. Models were developed for the entire training set and for a subset of the training set consisting of the most potent inhibitors. The optimized (RI) 4D-QSAR models are statistically significant (r(2) = 0.90, q(2) = 0.83 entire set, r(2) = 0.86, q(2) = 0.80 high potency subset) and also possess good predictivity based on test set predictions. The most and least potent inhibitors, in their respective postulated active conformations derived from the models, were docked in the active site of the TMPK crystallographic structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. This model identifies new regions of the inhibitors that contain pharmacophore sites, such as the sugar-pyrimidine ring structure and the region of the 5`-arylthiourea moiety. These new regions of the ligands can be further explored and possibly exploited to identify new, novel, and, perhaps, better antituberculosis inhibitors of TMPKmt. Furthermore, the 3D-pharmacophores defined by these models can be used as a starting point for future receptor-dependent antituberculosis drug design as well as to elucidate candidate sites for substituent addition to optimize ADMET properties of analog inhibitors.National Institutes of Health (NIH)[1 R21 GM075775

Molecular modeling study on the disassembly of dendrimers designed as potential antichagasic and antileishmanial prodrugs

A molecular modeling study was carried out to investigate the most likely enzymatic disassembly mechanism of dendrimers that were designed as potential antichagasic and antileishmanial prodrugs. the models contained myo-inositol (core), L-malic acid (spacer), and active agents such as 3-hydroxyflavone, quercetin, and hydroxymethylnitrofurazone (NFOH). A theoretical approach that considered one, two, or three branches has already been performed and reported by our research group; the work described herein focused on four (models A and B), five, or six branches, and considered their physicochemical properties, such as spatial hindrance, electrostatic potential mapping, and the lowest unoccupied molecular orbital energy (E (LUMO)). the findings suggest that the carbonyl group next to the myo-inositol is the most promising ester breaking point.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ São Paulo USP, Dept Pharm, LAPEN, Fac Pharmaceut Sci, BR-05508900 São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Exact & Earth Sci, Diadema, SP, BrazilFed Univ São Paulo UNIFESP, Dept Exact & Earth Sci, Diadema, SP, BrazilFAPESP: 01/01192-3FAPESP: 06/00116-5FAPESP: 07/59416-0FAPESP: 08/54211-4FAPESP: 07/5461-0Web of Scienc

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.FAPESP[01/01192-3]FAPESP[06/00116-5]FAPESP[07/59416-0]FAPESP[08/54211-4]FAPESP[07/5461-0]CNP

Lqta-qsar: A New 4d-qsar Methodology.

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.491428-3

QSAR Modeling of a Set of Pyrazinoate Esters as Antituberculosis Prodrugs

Tuberculosis is an infection caused mainly by Mycobacterium tuberculosis. A first-line antimycobacterial drug is pyrazinamide (PZA), which acts partially as a prodrug activated by a pyrazinamidase releasing the active agent, pyrazinoic acid (POA). As pyrazinoic acid presents some difficulty to cross the mycobacterial cell wall, and also the pyrazinamide-resistant strains do not express the pyrazinamidase, a set of pyrazinoic acid esters have been evaluated as antimycobacterial agents. In this work, a QSAR approach was applied to a set of forty-three pyrazinoates against M. tuberculosis ATCC 27294, using genetic algorithm function and partial least squares regression (WOLF 5.5 program). The independent variables selected were the Balaban index (I), calculated n-octanol/water partition coefficient (ClogP), van-der-Waals surface area, dipole moment, and stretching-energy contribution. The final QSAR model (N = 32, r(2) = 0.68, q(2) = 0.59, LOF = 0.25, and LSE = 0.19) was fully validated employing leave-N-out cross-validation and y-scrambling techniques. The test set (N = 11) presented an external prediction power of 73%. In conclusion, the QSAR model generated can be used as a valuable tool to optimize the activity of future pyrazinoic acid esters in the designing of new antituberculosis agents

4D-QSAR: Perspectives in Drug Design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure–activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design