Causal analysis of fetal death in high-risk pregnancies.

Objectives: To determine the causes of fetal death among the stillbirths using two classification systems from 22 weeks of gestation in a period of three years in high-risk pregnancies. This is a retrospective observational study.

Methods: The National Institute of Perinatal Health in Mexico City is a Level 3 care referral center attending high-risk pregnancies from throughout the country.

Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold.

Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD.

Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands.

Background: Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease.

Efficient and biologically relevant consensus strategy for Parkinson's disease gene prioritization.

Background: The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches.

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2AAdenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors.

Background: Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process.

Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human A3 adenosine receptor ligands.

Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives.

Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors.

Due to their role in the metabolism of monoamine neurotransmitters, MAO-A and MAO-B present a significant pharmacological interest. For instance the inhibitors of human MAO-B are considered useful tools for the treatment of Parkinson Disease. Therefore, the rational design and synthesis of new MAOs inhibitors is considered of great importance for the development of new and more effective treatments of Parkinson Disease.

Discovery of MAO-B inhibitors - present status and future directions part I: oxygen heterocycles and analogs.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The role of monoamine oxidase (MAO) inhibitors has expanded in the PD treatment. The present review will summarize the current structureactivity relationship information available on MAOs inhibitors of unrelated families of compounds of oxygen heterocyclic type based on coumarin, chromone and chalcone scaffolds.

Topological sub-structural molecular design approach: radical scavenging activity.

In the last decades phenolic compounds have gained enormous interest because of their beneficial health effects such as anti-inflammatory, anticancer, or antiviral activities. The pharmacological effects of phenolic compounds are mainly due to their antioxidant activity and their inhibition of certain enzymes. This antoxidant activity is related to the structure and has been extensively reported throught SAR or QSAR models.

Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models.

There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm.

Combining molecular docking and QSAR studies for modelling the antigyrase activity of cyclothialidine derivatives.

DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2), where GyrB catalyzes the hydrolysis of ATP. Cyclothialidine (Ro 09-1437) has been considered as a promising inhibitor whose modifications might lead to more potent compounds against the enzyme. We report here for the first time, QSAR studies regarding to ATPase inhibitors of DNA Gyrase.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach.

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals.

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.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds.

Objective: The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals.

Methods: Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds.

A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds.

Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity.

QSPR modelling with the topological substructural molecular design approach: beta-cyclodextrin complexation.

This study aims at developing a quantitative structure-property relationship (QSPR) model for predicting complexation with beta-cyclodextrins (beta-CD) based on a large variety of organic compounds. Molecular descriptors were computed following the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach and correlated with beta-CD complex stability constants by linear multivariate data analysis. This strategy afforded a final QSPR model that was able to explain around 86% of the variance in the experimental activity, along with showing good internal cross-validation statistics, and also good predictivity on external data.

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.

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.

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.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds.

Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, 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, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB).

Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set.

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 screening 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. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors.

QSAR studies using radial distribution function for predicting A1 adenosine receptors agonists.

The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation.

Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist.

In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools.

2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors.

The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training.

A topological substructural molecular design to predict soil sorption coefficients for pesticides.

A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices.