Nonlinear partial least squares modeling of phenyl alkylamines with the monoamine oxidase inhibitory activities

Identification of novel monoamine oxidase selective inhibitors employing a hierarchical ligand-based virtual screening strategy

Aim: Due to the pivotal role in the oxidative deamination of monoamine neurotransmitters, two distinct monoamine oxidase (MAO) subtypes, MAO-A and MAO-B, present a significant pharmacological interest. Here, we reported a hierarchical and time-efficient ligand-based virtual screening strategy to identify potent selective and reversible MAO inhibitors. Result: A total of 130 compounds were assessed in dose–response biochemical assay against MAOs. Among them, 70 compounds were active with inhibition higher than 70%, involving 25 compounds with IC50 values less than 1 μM. Conclusion: Our research demonstrated the validity of Biologically Relevant Spectrum (BRS-3D) in predicting subtype-selective ligands and afforded a novel highly efficient way to develop selective inhibitors in the early stage of drug discovery.

Computer-aided Drug Design Applied to Parkinson Targets

Background

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by debilitating motor deficits, as well as autonomic problems, cognitive declines, changes in affect and sleep disturbances. Although the scientific community has performed great efforts in the study of PD, and from the most diverse points of view, the disease remains incurable. The exact mechanism underlying its progression is unclear, but oxidative stress, mitochondrial dysfunction and inflammation are thought to play major roles in the etiology.

Objective

Current pharmacological therapies for the treatment of Parkinson’s disease are mostly inadequate, and new therapeutic agents are much needed.

Methods

In this review, recent advances in computer-aided drug design for the rational design of new compounds against Parkinson disease; using methods such as Quantitative Structure-Activity Relationships (QSAR), molecular docking, molecular dynamics and pharmacophore modeling are discussed.

Results

In this review, four targets were selected: the enzyme monoamine oxidase, dopamine agonists, acetylcholine receptors, and adenosine receptors.

Conclusion

Computer aided-drug design enables the creation of theoretical models that can be used in a large database to virtually screen for and identify novel candidate molecules.

Privileged scaffolds as MAO inhibitors: Retrospect and prospects

This review aims to be a comprehensive, authoritative, critical, and readable review of general interest to the medicinal chemistry community because it focuses on the pharmacological, chemical, structural and computational aspects of diverse chemical categories as monoamine oxidase inhibitors (MAOIs). Monoamine oxidases (MAOs), namely MAO-A and MAO-B represent an enormously valuable class of neuronal enzymes embodying neurobiological origin and functions, serving as potential therapeutic target in neuronal pharmacotherapy, and hence we have coined the term "Neurozymes" which is being introduced for the first time ever. Nowadays, therapeutic attention on MAOIs engrosses two imperative categories; MAO-A inhibitors, in certain mental disorders such as depression and anxiety, and MAO-B inhibitors, in neurodegenerative disorders like Alzheimer's disease (AD) and Parkinson's disease (PD). The use of MAOIs declined due to some potential side effects, food and drug interactions, and introduction of other classes of drugs. However, curiosity in MAOIs is reviving and the recent developments of new generation of highly selective and reversible MAOIs, have renewed the therapeutic prospective of these compounds. The initial section of the review emphasizes on the detailed classification, structural and binding characteristics, therapeutic potential, current status and future challenges of the privileged pharmacophores. However, the chemical prospective of privileged scaffolds such as; aliphatic and aromatic amines, amides, hydrazines, azoles, diazoles, tetrazoles, indoles, azines, diazines, xanthenes, tricyclics, benzopyrones, and more interestingly natural products, along with their conclusive SARs have been discussed in the later segment of review. The last segment of the article encompasses some patents granted in the field of MAOIs, in a simplistic way.

Statistical approaches to functional neuroimaging data

The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiments and providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objectives include detecting brain regions that reveal task-related alterations in measured brain activity (activations) and identifying highly correlated brain regions that exhibit similar patterns of activity over time (functional connectivity). This article highlights various statistical procedures for analyzing data from activation studies and functional connectivity studies, focusing on functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data. Also discussed are emerging statistical methods for prediction using fMRI and PET data, which stand to increase the translational significance of functional neuroimaging data to clinical practice.

Partial least squares analysis of neuroimaging data: applications and advances

Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroimaging.

Prediction of programmed-temperature retention values of naphthas by artificial neural networks

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.