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  • Open access
  • 35 Reads
Multivariate classification of a series of organic compounds of pharmaceutical interest using MODESLAB methodology

Due to the high cost of the development of new active pharmaceutical ingredients and excipients for the pharmaceutical industry, molecular modeling methods have been included in this process more and more frequently in recent years. In this work the calculation of the spectral moments of the matrix of adjacency between edges of the molecular graph with suppressed hydrogens was made, weighted in the main diagonal with different parameters that characterize both the bonds and the atoms in the molecules of compounds of pharmaceutical interest, using the MODESLAB methodology. 91 descriptors related to solubility were calculated, which were used in a training series divided into five groups, according to the priority rules of the IUPAC. With the aim of identifying the descriptors that best discriminate between the compounds of each group and defining the set of functions of these descriptors able to distinguish with the greatest possible precision the members of one or the other group, a discriminant analysis was developed using the stepwise inclusion method using the statistical software IBM SPSS version 22. Four functions were generated that constitute combinations linear of 16 molecular descriptors, which encode both steric and electronic information of the molecules of each group. The functions obtained have a very low minimum Wilks Lambda (0.067) and a high canonical correlation (0.89), which demonstrates their discriminant power and allows the use of the descriptors included in them in future studies of structure-property or structure-relationship. activity.

  • Open access
  • 28 Reads
Modeling and prediction of anti-inflammatory activity in compounds of natural and synthetic origin

In the work the MODESLAB approach is applied to the sub-structural modeling of the anti-inflammatory activity of both natural and synthetic compounds, with the purpose of calculating the spectral moments of the adjacency matrix between edges of the molecular graph with suppressed hydrogens , weighted in the main diagonal with standard dipole moments of binding to 410 active and inactive compounds. The calculated descriptors were used in the design of a training series and another one of prediction. With the training series a discriminant function was developed for the anti-inflammatory activity by means of the Multivariate Linear Regression Discriminant analysis obtaining a good total classification of 91.59%. The model was validated through the use of the prediction series, obtaining a good classification of 90.2%.

  • Open access
  • 16 Reads

El sistema económico de mercado tiene como consecuencia la relación entre consumidores y usuarios. Dicha relación está basada en una desigualdad de inicio, que posiciona al empresario como un sujeto de poder en detrimento del consumidor. Ante esto, el Derecho establece mecanismos jurídicos de protección a la parte débil. El presente trabajo tiene como objeto un estudio sucinto sobre parte de los mecanismos que el Derecho Penal instaura para la protección del consumidor: el artículo 281 y 283 del Código Penal.

  • Open access
  • 19 Reads
Post-emergence herbicidal activity of Eucalyptus globulus Labill. essential oil

Weed resistances to synthetic herbicides, as well as consequent health and environmental problems, are important items to find more eco-friendly natural alternatives to weed control. Eucalyptus globulus Labill. essential oil has been traditionally used against respiratory troubles as well as insect repellent due to 1,8-cineole content. Chemical composition of commercial E. globulus essential oil and its phytotoxic activity against three common annual weeds (Portulaca oleracea L., Echinochloa crus-galli (L) Beau. and Lolium multiflorum Lam.) has been studied. Twenty-eight compounds reaching 99.83% of the total essential oil were identified by gas chromatography-mass spectrometry analysis. The oxygenated monoterpene 1,8-cineole (76.43±0.35%), followed by the monoterpene hydrocarbon α-pinene (14.64±0.27%) were the main compounds. E. globulus essential oil lacks of phytotoxicity against the seed germination of the tested weed, showing significant effect on hypocotyl and radicle elongation of E. crus-galli at the highest dose (1 µL/mL) assayed and radicle inhibitory effects at all concentrations applied (0.125, 0.25, 0.50 and 1 µL/mL) against L. multiflorum. E. globulus essential oil could be used in the management of E. crus-galli due to its post-emergence herbicidal activity.

  • Open access
  • 27 Reads
PTML Model Prediction of Preclinical Activity

ChEMBL-tik datu basea lortuta, perturbazio teoria (PT) eta Machine Learning (ML) teknikak erabilita PTML eredu bat eraiki da, zein sistema biomolekular konplexuetan erabili daitekeen perturbazioen efektua kuantifikatzeko.

Eredu hau erabilita konposatu berri batek erakusten dituen minbiziaren aurkako parametro klinikoen (ki, LD50, etab.) balioak aurresan ditzakegu.

After obtaining the database from ChEMBL we combine Perturbation Theory (PT) and Machine Learning (ML) to obtain PTML Model, which has been created to quantify the perturbations of complex bio molecular systems. The model can predict preclinical (ki, LD50, etc.) values of new anti-cancer compounds.

  • Open access
  • 19 Reads
Phytochemical Profiling and Antioxidant Activity of Aqueous Extract of Aegle marmelos Fruit Shell

Plants are having many constituents which contain medicinal properties and they act as reservoir for such valuable compounds. In this article we are exploring the phytoconstituents and antioxidant potential of aqueous extract, prepared from hard shell of fruit of Aegle marmelos (Bael). The aqueous extract (AE) was analyzed for phytoconstituents by phytochemical profiling and by measurement of total phenolic content. The potency of extract was evaluated through various in-vitro assays like total reducing power, DPPH free radical scavenging activity, superoxide radical scavenging activity, hydroxyl radical scavenging activity, lipid peroxidation inhibition and metal chelation activity. Antimicrobial activity and hemolytic activity was also evaluated to assess its potential for further use in therapeutics.

  • Open access
  • 18 Reads
Isolation of Novel Hyaluronidase Inhibitor from the Hard Shell of Coconut

In the current study, we have isolated and characterized a novel molecule from the hard shell of Cocos nucifera Linn. and evaluated it for its inhibitory potential against hyaluronidase enzyme. Alcoholic extract of the hard shell was subjected to various purification procedures viz. column chromatography, solvent based extraction and TLC to yield a phytomolecule. Spectral characterization indicated that it is a novel keto fatty acid. To the best of our knowledge, this is the first keto fatty acid from the coconut plant even. Results of hyaluronidase inhibition assay indicated that it has moderate hyaluoronidase inhibitory activity.

  • Open access
  • 32 Reads
Gaussian basis set of triple zeta quality for atoms Fr through Lr: Application in DFT calculations of molecular properties

Segmented all-electron basis sets of valence triple zeta qualities plus polarization functions for the elements Fr to Lr are generated using non-relativistic and Douglas-Kroll-Hess (DKH) Hamiltonians. The sets are augmented with diffuse functions with the purpose to describe appropriately the electrons far from the nuclei. At the DKH-B3LYP level, bond lengths and dissociation energies of a sample of diatomics are calculated for Fr, Ra and Ac. For the actinide monoxides, bond distances and dissociation energies are calculated with the B3LYP/ TZP-DKH procedure. Comparison with theoretical and experimental data available in the literature is carried out. It is verified that despite the small sizes of the basis sets, they are yet reliable.

  • Open access
  • 18 Reads
Inhibition of Staphylococcus aureus and its biofilm by the metabolites of endophytic Streptomyces sp. ADR1

Staphylococcus aureus is a gram positive, tissue colonizer pathogen in humans. It is known for its tendency to build up biofilm which is a major cause of antibiotic resistance. To overcome this problem, there is an urgent requirement to discover novel antimicrobial compounds against new bacterial targets and drug resistance. In this direction the actinobacteria inhabiting special niche like plant tissues can be promising agents for novel compounds against methicillin sensitive and resistant S. aureus (MRSA).

The ethyl acetate extract of Streptomyces sp. ADR1 is found to be a strong inhibitor of various Staphylococcus sp. and its resistant strain MRSA with very low MIC90 values; <31.25 µg/ml. The extract was found to inhibit biofilm formation as well as preformed biofilms of S. aureus and MRSA to a significant extent.

  • Open access
  • 36 Reads
PTML: Perturbation-Theory Machine Learning notes

PTML: Perturbation-Theory Machine Learning methods have been developed by Humbert Gonzalez-Diaz et al [0] to seek models able to predict multiple properties f(si, cj)k of type k of a system (si) at the same time (multi-output and multi-objective) taking into consideration variations (perturbations) in multiple experimental conditions cj = (c0, c1, c2, ..... cn) at the same time with respect to a value of reference or expected. PTML-like models have been applied for different authors to study drugs, proteins, nanoparticles, complex networks, social systems, etc. [1-17]. In the particular case of a PTML linear models we can fit an equation with the general form f(si, cj)new = a0 + a1· f(si, cj)ref + SUM(bk·PTO(cj)k). In this model PTO(cj)k are PT operators measuring the perturbations in the new system si with respect to the system of reference sr with observed or expected property f(si, cj)ref. First, we need to calculate the values of the PTOs in the data pre-processing step. This PTOs allow us to perform an Information Fusion process with variables and conditions from different sources. Moving Averages (MA), Multi-condition MA (MMAs), Double MAs, Co-variance Operators, etc. are some examples of useful PTOs. After that, we can use Multiple Linear Regression (MLR), Linear Discriminant Analysis (LDA), or other linear ML techniques to seek the PTML model. In the non-linear cases, we can fit the PTML models using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Classification Trees, and other ML methods.
General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. González-Díaz H, Arrasate S, Gómez-SanJuan A, Sotomayor N, Lete E, Besada-Porto L, Ruso JM. Curr Top Med Chem. 2013;13(14):1713-41.
Simón-Vidal L, García-Calvo O, Oteo U, Arrasate S, Lete E, Sotomayor N, González-Díaz H. Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies. J Chem Inf Model. 2018 Jul 23;58(7):1384-1396. doi: 10.1021/acs.jcim.8b00286.
Ferreira da Costa J, Silva D, Caamaño O, Brea JM, Loza MI, Munteanu CR, Pazos A, García-Mera X, González-Díaz H. Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics. ACS Chem Neurosci. 2018 Jun 25. doi: 10.1021/acschemneuro.8b00083.
Luan F, Kleandrova VV, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MN. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale. 2014 Sep 21;6(18):10623-30. doi: 10.1039/c4nr01285b.
Martínez-Arzate SG, Tenorio-Borroto E, Barbabosa Pliego A, Díaz-Albiter HM, Vázquez-Chagoyán JC, González-Díaz H. PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical-Experimental Study of Bm86 Protein Sequences from Colima, Mexico. J Proteome Res. 2017 Nov 3;16(11):4093-4103. doi: 10.1021/acs.jproteome.7b00477.