Structure- and ligand-based drug design approaches for neglected tropical diseases

Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. Structure( SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Structure of trajectories of complex-matrix eigenvalues in the Hermitian-non-Hermitian transition

The statistical properties of trajectories of eigenvalues of Gaussian complex matrices whose Hermitian condition is progressively broken are investigated. It is shown how the ordering on the real axis of the real eigenvalues is reflected in the structure of the trajectories and also in the final distribution of the eigenvalues in the complex plane.

Determination of Opiates in Whole Blood and Vitreous Humor: A Study of the Matrix Effect and an Experimental Design to Optimize Conditions for the Enzymatic Hydrolysis of Glucuronides

Faculty of Medicine University of Sao Paulo

Robust and optimal adjustment of Power System Stabilizers through Linear Matrix Inequalities

This work presents the application of Linear Matrix Inequalities to the robust and optimal adjustment of Power System Stabilizers with pre-defined structure. Results of some tests show that gain and zeros adjustments are sufficient to guarantee robust stability and performance with respect to various operating points. Making use of the flexible structure of LMI's, we propose an algorithm that minimizes the norm of the controllers gain matrix while it guarantees the damping factor specified for the closed loop system, always using a controller with flexible structure. The technique used here is the pole placement, whose objective is to place the poles of the closed loop system in a specific region of the complex plane. Results of tests with a nine-machine system are presented and discussed, in order to validate the algorithm proposed. (C) 2012 Elsevier Ltd. All rights reserved.

Ligand- and Structure-Based Drug Design Strategies and PPAR delta/alpha Selectivity

Peroxisome-proliferator-activated receptors are a class of nuclear receptors with three subtypes: a, ? and d. Their main function is regulating gene transcription related to lipid and carbohydrate metabolism. Currently, there are no peroxisome-proliferator-activated receptors d drugs being marketed. In this work, we studied a data set of 70 compounds with a and d activity. Three partial least square models were created, and molecular docking studies were performed to understand the main reasons for peroxisome-proliferator-activated receptors d selectivity. The obtained results showed that some molecular descriptors (log P, hydration energy, steric and polar properties) are related to the main interactions that can direct ligands to a particular peroxisome-proliferator-activated receptors subtype.

Structure-based drug design studies on a series of aldolase inhibitors

Human African trypanosomiasis, also known as sleeping sickness, is a major cause of death in Africa, and for which there are no safe and effective treatments available. The enzyme aldolase from Trypanosoma brucei is an attractive, validated target for drug development. A series of alkyl‑glycolamido and alkyl-monoglycolate derivatives was studied employing a combination of drug design approaches. Three-dimensional quantitative structure-activity relationships (3D QSAR) models were generated using the comparative molecular field analysis (CoMFA). Significant results were obtained for the best QSAR model (r2 = 0.95, non-cross-validated correlation coefficient, and q2 = 0.80, cross-validated correlation coefficient), indicating its predictive ability for untested compounds. The model was then used to predict values of the dependent variables (pKi) of an external test set,the predicted values were in good agreement with the experimental results. The integration of 3D QSAR, molecular docking and molecular dynamics simulations provided further insight into the structural basis for selective inhibition of the target enzyme.