The vitamin D receptor is a common target for various drugs, and is of great interest due to the protective function that this vitamin exerts on the body. The presence of single nucleotide polymorphisms (SNPs) in this receptor can affect the binding of the drug, which makes its analysis important. Through chemoinformatic studies, Perturbation Theory Machine Learning Information Fusion (PTMLIF) models that analyze this interaction can be established, for which the drug data set was downloaded from the public databases ChEMBL and NCBI (National Center for Biotechnology Information) and was subsequently performed the fusion of information. The database included 26064 trials with 47 different properties and 376 SNPs. In the present study, the deviations of the reference drug with respect to the perturbation operators were measured. The best model obtained showed values ​​of Sp = 72.62%, Sn = 89.54% and Ac = 83.85% for training and Sp = 74.78% Sn = 90.88% and Ac = 85.31% for validation for a given application domain.

Abstract

The main objective of the present work was to study the aptitude to drying of Pulicaria odora L. leaves pre-coated in functional gels. This involves drying in the open air leaves of P.odora pre-coated in a carrageenan gel, a carrageenan-based gel added to Z. jujuba Mill peel powder. In addition, the evaluation of certain properties (physicochemical, rheological and biological) of the obtained powders was carried out.

The obtained results show that coating has a very positive effect on the rheological properties of P. odora leaves powder (small grain size, good flow, and little swelling). It also promotes the conservation and release of bioactive substances (polyphenols and flavonoids). Indeed, the best extraction rates were obtained for the powder from the leaves coated in carrageenan gel based Z. jujuba Mill peel powder with levels of 1099.996 ± 8.545 mg EAG /g d.b and 141.336 ± 0.89 mg quercetin / g d.b) respectively for total polyphenols and flavonoids. Finally, the test for the antimicrobial activity of the total polyphenol extracts of the obtained powders reveals that they are effective against Gram-positive bacteria (S.aureus ATCC 25923) and Gram-negative bacteria (E.coli ATCC 25322) and yeast (C.albicans). The ethanolic extract of the powder from the leaves coated in the carrageenan gel based Z.jujuba Mill peel powder has an inhibition diameter of 24.5 ± 0.15 mm with respect to C.albicans.

We provide a method for experimental data smoothing under a certain noise by using a statistical fitting considering gaussian weight functions. On the one hand, this method is quite useful when we have a large amount of experimental data, which are expected to approach an unknown theoretical curve. This allows us to find quite closely the derivative of the theoretical curve from the data and provides as well the error in the numerical integration of the data. On the other hand, the proposed method improves the typical smoothening of the time series of financial data and allows the calculation of the volatility as a function of time.

Abstract

The allosteric modulator performs the function of allosteric regulation, which indirectly increases or decreases the effect of an agonist or antagonist on a cellular receptor by activating a catalytic site on the protein[1]. Allostery can both cause diseases and this involves synthesizing drugs with higher selectivity and less toxicity, to fit into the primary active center (orthosteric) of the biological objectives, in order to induce a therapeutic effect. [2] In this study we have employed Perturbation Theory (Pt) ideas and Machine Learning techniques (ML) to seek a PTML model of the ChEMBL database for allosteric modulators. In this case, the Linear Discriminant Analysis (LDA) has been used to develop this model. This aims to predict the probability of allosteric activity for more than 20000 preclinical tests, leading to very good results of statistical parameters: Specificity Sp = 87.61 / 87.51% and sensitivity Sn = 75.18 / 75.35 % in training / validation series.

[1] Monod, J.; Wyman, J.P.: On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology 1965, 12, 88-118.

[2] Nussinov, R.; Tsai, C. J.: Allostery in disease and in drug discovery. Cell 2013, 153, 293-305.

The number of possible designs of nano-systems is elevated. The design depends on the function we need to develop. Among these systems we highlight Nanoparticle Drug Delivery Systems (DDNS) of high interest not only for Nanotechnology but also for Biomaterials science.^1–3

In this work we fusion the following information: 1) Drug-vitamin release nano-systems (DVRNs). This data set was collected from literature. 2) Vitamin derivatives data set extracted from ChEMBL database. Both data sets contain different assay conditions and molecular descriptors. Once we fusion the information, we apply Perturbation Theory Machine Learning (PTML) method in order to build the model. Once built with Perturbation Theory Operators (PT Operators), it presents both Specificity and Sensibility higher than 80%.

Until the best of our knowledge, we developed the first multi-label PTML model useful to design DVRNs for optimal biological activity.

Due to the high rate of resistance and the frequent relapse after treatment, Mexican

Leishmania, the causative agent of cutaneous leishmaniasis in countries such as Mexico and

Central America, constitutes a health problem and the search for new therapies is necessary.

Hydroxyurea, a cancer drug, has been shown to be effective in stopping the main cell cycle

of Leishmania. Martínez-Rojano H and collaborators carried out a study where said drug was

tested in an in vitro model of infection in macrophages. Meglumine antimony, standard

pharmacological treatment for Leishmania mexicana, was used as a reference under the same

experimental conditions. The hydroxyurea completely eliminated the Leishmania parasites

when used at a dose of 10 or 100 microg / ml, with a difference in the duration of treatment

of 9 and 3 days respectively. More recent studies have shown that 2 and 3-hydroxypyridine

hydroxyalkyl and acyloxyalkyl derivatives show inhibitory activity against the growth of

Mexican Leishmania. García Liñares G and collaborators obtained thirty new compounds by

means of a chemoenzymatic methodology in two reaction stages. The influence of

parameters such as enzyme source, acylating agent / substrate ratio, enzyme / substrate ratio,

solvent and temperature on the enzymatic reaction was evaluated. Acetylated derivatives

showed greater efficacy in inhibiting the growth of Mexican Leishmania. On the other hand,

Mendoza-Martínez C synthesized a series of quinazoline-2,4,6-triamine and evaluated it in

vitro against Leishmania mexicana. N (6) - (Ferrocenmethyl) quinazolin-2,4,6-triamine (H2)

showed activity in intracellular promastigotes and amastigotes, in addition to low

cytotoxicity in mammalian cells. The study showed the importance of the ferrocene nucleus

and the heterocyclic nucleus for the observed activity, in addition to indicating that the

mechanism of action involves redox reactions due to the easy oxidation of H2.

Degenerative neurological diseases have become serious risks to human health. These diseases depend on age and are becoming more common today, as the number of older people in society increases. The discovery of new drugs for the treatment of neurodegenerative diseases such as Alzheimer's, Parkison’s, and Huntington's diseases, Friedreich ataxia and others is an important goal of medicinal chemistry. For this reason, it is very useful to use the existing public information on preclinical assays with a high number of combinations of experimental conditions to create models that allow predicting new compounds useful for the treatment of these diseases. ChEMBL is a chemical database of bioactive molecules with drug-like properties. This database manages Big Data feature with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for neurodegenerative diseases. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. The PT operators used are founded on multi-condition moving averages, combining different features and simplifying the difficulty to manage all data. For the construction of this model, the structure of the drug, the sequence of the proteins with which these drugs interact, the protein interaction network and the brain region in which these proteins are expressed were considered. The bondaring conditions that were taken into account were: the activity of the drug, the cell line in which the drug was tested, the brain region and the test organism. The developed PTML model reached considerable values in sensibility (80.89% for training and 80.94% for validation), specificity (80.18% for training and 80.33% for validation), and accuracy (80.25% for training and 80.39% for validation). We can conclude that this PTML model is the first one that can predict the activity of drug candidate compounds against degenerative neurological diseases taking into account the structure of the drug, the sequence of the proteins with which these drugs interact, the protein interaction network and the brain region in which these proteins are expressed.

The Quantitative Structure-Activity Relationship (QSAR) models are a very useful tool in the design of new chemical compounds. The QSAR methods are based on the assumption that the activity of a certain chemical compound is related to its structure. Two types of QSAR analysis are summarized in this review: Linear Regression model (LR) and Linear Discriminant Analysis model (LDA).

The main objective of this project is the development of a useful computational tool for future preclinical trials. The implemented model will be a PTML-LFER model, for the prediction of the pharmacological activity of a certain molecule, or list of molecules, under multiple test conditions.

Using the solid-state method, we were synthesized ZnO/ZnNb₂O₆ composite. The elaborated composite was characterized by X-ray diffraction and impedance spectroscopy. The XRD patterns show the co-existence of a hexagonal ZnO and an orthorhombic ZnNb₂O₆ structures. Then, the dielectrically properties were investigated at room temperature in a wide range of frequencies (from 20 Hz to 1MHz). Moreover, the composite nyquist curve revealed the contribution of grain and grain-boundaries. Indeed, theoretical fit exhibits high resistance, capacitive behavior, high permittivity and low loss factor. So this composite is a good candidate for super capacitor for energy storage application.