Nanotechnology has been an important tool for the production of nanoparticles with controlled release of drugs for therapeutic applications. Here, we produced solid lipid nanoparticles (SLN) loaded with curcumin and capsaicin (NCC) following the overarching goals of green chemistry. Currently, besides evaluating the composition, and size of these, it is necessary to understand the interactions between nanoparticles and the biomolecules present in the biological medium. For this, assays were conducted in order to evaluate the potential formation of the protein 'corona', and to better understand the results obtained in vitro, we also performed an interaction study, in silico, between the NCC components and the main serum protein, albumin. In the first hour of contact between the NCC and the culture medium showed fluctuation in the diameter of the NCC. However, after 24 and 48 h of the incubation period, all NCC concentrations showed an increase in size, which can be attributed to plasma protein adsorption. Since, hard corona takes a few seconds, while the soft corona can be formed in minutes up to a few hours. On the other hand, best docking binding-poses of interaction for the formed docking complexes evaluated suggest interactions following the docking affinity like free energy FEB (Tween 80bovine serum albumin) approximate to FEB (Span 80-bovine serum albumin) showing a pharmacodynamic pattern based in non-covalent hydrophobic interactions with the bovine serum albumin binding-site. Our in silico results clarify and reinforce our in vitro findings of corona formation, which represents the real interaction with cell membranes in vivo.

Despite a growing number of research reports on neat room temperature ionic liquids (RTILs) and their mixtures with molecular solvents in recent years, understanding and rationalising of such systems is still a challenge. In this work, we performed a classical molecular dynamics simulation study of the pure components - 1-ethyl-3-methylimidazolium thiocyanate (C2C1 imSCN), methanol, and ethanol - and their binary mixtures at room temperature. Thermodynamic (density and heats of vaporization), transport (viscosity and self-diffusion coefficients) and structural (in terms of radial, angular and spatial distributions) properties were analysed. It was found, that with the decrease of RTIL content, the ions self-diffusion coefficients notably increase, reaching higher values in the C2C1 imSCN-MeOH system. Density and viscosity follow the opposite trend, reaching their minimum at lower RTIL mole fraction. Negative deviations of excess molar volume from ideality in the studied mixtures with minima at similar to 0.2-03 mole fraction of RTIL suggest the strongest ion-molecular interactions at this mixture composition. A careful analysis at the molecular level revealed that introducing of alcohols to both systems weakens the inter-ionic H-bonding network, particularly, at low RTIL content. The cation-cation arrangement was found to lose its characteristic above/below orientation in neat RTIL and become disordered at low RTIL content. As to the tail length of the selected alcohols, this was found to have an insignificant effect on the structural properties of the addressed systems.

Glutamate plays an important role in excitatory neurotransmission, learning, and memory processes, and under pathological conditions it is directly associated with several chronic neurological disorders, such as depression, epilepsy, schizophrenia, and Parkinson's. Therefore, the detection and quantification of Glutamate is important for the rapid diagnosis of these diseases. Using first principles and molecular docking simulations we have evaluated the energetic, structural, and binding properties of graphene derivatives, such as pristine graphene (pristine-Gr) and oxidized graphene with carboxylic (Gr-COOH), carbonyl (Gr-COH), hydroxyl (Gr-OH), and epoxy (-O-) groups interacting with the glutamate neurotransmitter. The calculated binding affinity free energies from the docking complexes (glutamate-graphene family) suggest higher oxidized graphene-based glutamate molecular recognition than the pristine-Gr, with the following order of oxidized graphene derivatives according to ab initio results: (Gr-O-Gr-COOH - Gr-COH > Gr-OH)>pristine-Gr. Herein, the ab initio binding energies found for the glutamate-graphene family complexes are in the range of 0.24-0.80 eV. The configurations studied showed a biophysical adsorption regime without significant changes in the physico-chemical properties of the adsorbed glutamate neurotransmitter, in accordance with the general acceptance criteria of the detection systems.

In this work we report a new potential model for classical molecular dynamics (MD) simulation of two dipolar aprotic solvents - propylene carbonate (PC) and gamma-butyrolactone (GBL). Parameters for intramolecular interactions as well as the partial atomic charges were derived from quantum chemical calculations, while the OPLS/AA force field Lennard-jones intermolecular parameters were adjusted to reproduce the experimental thermodynamic and the transport properties of these solvents in the wide temperature range. The comparison of new potential models with existing models for PC and GBL is given. Using the proposed models, the local structure of these solvents was studied in detail, paying special attention to packing ability, dipole-dipole orientation and hydrogen-bonding (H-bonding) interactions. Based on the results of MD simulations and QTAIM analysis, it was shown that H-bonds involving carbonyl oxygen atoms in PC and GBL are rather weak, while no H-bonds are formed with ester oxygen atoms. According to the present results, the mutual molecular orientation in PC and GBL varies with the distance, being above/below plane-parallel configuration for closest neighbors, and in-plane T-shaped configurations for farther neighbors. The nearest neighbors approach coupled with angular distribution functions was applied for the estimation of the Kirkwood factor in the framework of the OnsagerKirkwood-Frohlich theory. Our results shows that in order to reproduce the corresponding experimental values the Kirkwood factor it is necessary to take in account the mutual orientation of similar to 8-9 neighboring molecules for GBL and similar to 5-6 molecules for PC. (C) 2019 Published by Elsevier B.V.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp approximate to Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

In the present study, the molecular docking mechanism based on pharmacodynamic interactions between the ligands AZD1208 and recognized chemotherapy agents (Vincristine and Daunorubicin) with human ATP-binding cassette (ABC) transporters (ABCB1) was investigated. For the first time, were combined an in silico approaches like molecular docking and ab initio computational simulation based on Density Functional Theory (DFT) to explain the drug-drug interaction mechanism of aforementioned chemotherapy ligands with the transmembrane ligand extrusion binding domains (TMDs) of ABCB1. In this regard, the theoretical pharmacodynamic interactions were characterized by using the Gibbs free energy (FEB, kcal/mol) from the best ABCB1-ligand docking complexes. The molecular docking results pointing that for the three chemotherapy ABCB1-ligand complexes are mainly based in non-covalent hydrophobic and hydrogen-bond interactions showing a similar toxicodynamic behavior in terms of strength of interaction (FEB, kcal/mol) and very close free binding energies when compared with the FEB-values of the ABCB1 specific-inhibitor (Rhodamine B) = -6.0 kcal/mol used as theoretical docking control to compare with FEB (AZD1208-ABCB1) similar to FEB (Vincristine-ABCB1) similar to FEB (Daunorubicin-ABCB1) -6.2 kcal/mol as average. Ramachandran plot suggests that the 3D-crystallographic structure from ABCB1 transporter can be efficiently-modeled with conformationally-favored Psi versus Phi dihedral angles for all key TMDs-residues. Though, the results of DFT-simulation corroborate the existence of drug-drug interaction between (AZD1208/Vincristine) > (AZD1208/Daunorubicin). These theoretical pieces of evidence have preclinical relevance potential in the design of the new drugs to understand the polypharmacology influence in the molecular mechanism of multiple-drugs resistance, contributing with a higher success in chemotherapy and prognosis of cancer patients.

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure-activity relationship (mt-QSAR) models based on the Box-Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.