Computational Quantum Chemical Study, Drug-Likeness and In Silico Cytotoxicity Evaluation of Some Steroidal Anti-Inflammatory Drugs

This paper contains a theoretical study of ten Anti-inflammatory steroids (AIS) on the understanding of the relationship between the structure and activity of the drug, the pharmacokinetic parameters responsible for bioavailability and bioactivity and finally the toxicity evaluation. DFT calculations with B3LYP/6-31G (d, p) level have been used to analyze the electronic and geometric characteristics deduced for the stable structure of the compounds. Moreover, using the Frontier Molecular orbital (FMO) energies, MEP surface visualizations and the density-based descriptors such as chemical potential (µ), electronegativity (χ), hardness (η) and softness (σ), the chemical stability were determined. Furthermore, in silico, studies showed that Lipinski rules are applied, which means that these (AIS) are expected to have a high probability of good oral bioavailability. On the other side, the bioinformatic Osiris/Molinspiration analyses of the relative cytotoxicity of these derivatives are reported in comparison to Cortisol. In fact, it has been showed that almost of these compounds are non-toxics except for Mometasone that presents a great risk of tumorigenicity during reproduction with a slightly mutagenic structure due to the two chloride atoms. from all results obtained, we can conclude that fluticasone has the best physico-chemical properties which explains its high efficiency. Keywords: Anti-inflammatory steroids, DFT, Lipinski rules, Tumorigenicity

Designing, Cytotoxic Evaluation, Molecular Docking and in Silico Pharmacokinetic Prediction of New Hydrocortisone Derivatives as Anti-Asthmatics Drugs

A series of new 20 corticosteroids were subjected to molecular property prediction. The Molecular, Physicochemical, and Biological properties were determined using Molinspiration Cheminformatics software. These compounds were further subjected to Toxicity Predictions using the Osiris Software. The calculated drug-related properties of the designed molecules were similar to those found in most marketed drugs. Amongst the proposed molecules, fourteen promising candidates can be considered as promising structures for the synthesis of new and more effective anti-asthmatic drugs. Result indicates that the derivatives are orally active molecules.  In-silico ADME and toxicity prediction was accomplished with the help of Swiss-ADMET tool provides the latest and most inclusive for diverse chemicals associated with known Absorption, Distribution, Metabolism, Excretion and Toxicity profiles. furthermore, BBB (Blood brain barrier) penetration, HIA (Human intestinal absorption), Caco-2 cell permeability and Ames test were calculated using ADMET web-based query tools incorporating a molecular build in interface enable the database to be queried by Smiles and structural similarity search. According to molecular docking results, derivatives No 4, 10 and 11 showed better docking Scores values compared to other derivatives and also dexamethasone and hydrocortisone. Keywords: Corticosteroids, Drug-likeness, Lipophilicity, Anti-asthmatic, ADME

Computational fluid dynamics study of the dry reforming of methane over Ni/Al2O3 catalyst in a membrane reactor. Coke deposition

International audienceThis work investigates the dry reforming of CH4 as an important process for the conversion of greenhouse gases to synthesis gas. The mixture of methane and CO2 is readily available in the greenhouse gas which makes realization of dry reforming of methane process more convenient. The paper is an attempt to numerically analyse by computational fluid dynamics (CFD) the coking and gasification mechanisms in the lab-scale membrane module with a fixed-bed supported nickel catalyst (Ni/Al2O3). The concentrations and molar fluxes obtained by the simulation are compared with the experimental profiles to validate the CFD model. It was found that working in a catalytic fixed-bed membrane reactor, in the case of the dry reforming of methane and under specific conditions, was not critical, from the point of view of catalyst deactivation

Methane Dry Reforming over Ni-Co/Al2O3: Kinetic Modelling in a Catalytic Fixed-bed Reactor

International audienceThe dry reforming of CH4 was investigated in a catalytic fixed-bed reactor to produce hydrogen at different temperatures over supported bimetallic Ni-Co catalyst. The reactor model for the dry reforming of methane used a set of kinetic models: The Zhang et al model for the dry reforming of methane (DRM); the Richardson-Paripatyadar model for the reverse water gas shift (RWGS); and the Snoeck et al kinetics for the coke-deposition and gasification reactions. The effect of temperatures on the performance of the reactor was studied. The amount of each species consumed or/and produced were calculated and compared with the experimental determined ones. It was showed that the set of kinetic model used in this work gave a good fit and accurately predict the experimental observed profiles from the fixed bed reactor. It was found that reaction-4 and reaction-5 could be neglected which could explain the fact that this catalyst coked rapidly comparatively with other catalyst. The use of large amount of Ni-Co will lead to carbon deposition and so to the catalyst deactivation

Influence of chemical reaction on electro-osmotic flow of nanofluid through convergent multi-sinusoidal passages

Aim: In the present study, the influences of chemical reactions on heat transfer and the peristaltic pumping of nanofluid in a convergent channel are analyzed. This mathematical analysis is looked at under the postulates of greater wavelength and smaller Reynold's number. Research methodology: The governing equations are initially transformed from fixed to a wave frame by using linear transformations. Furthermore, these transformed equations are non-dimensioned with the help of similarity variables. Due to the complex form of non-dimensional flow equations, numerical solutions for velocity profile, stream function, temperature profile, and nanoparticle concentration are obtained with the help of Mathematica 11.0 software. These numerical solutions are described via graphs in Mathematica software. These numerical solutions are plotted for numerous rheological parameters. The transportation of nanofluid is based upon multi-sinusoidal natures (cosine wave, sine wave, triangular wave, square wave, sawtooth wave) of peristaltic waves. Outcomes: It is found that with an increasing heat source parameter, the channel flow is decelerated due to the magnitudes of velocity profile and stream function are reduced. While sharp enhancements are noticed in both temperature and nanoparticle concentration by increasing the heat source parameter under chemical reaction effects. The high temperature is obtained with larger chemical reactions. The contrast among viscous and non-viscous fluids is also addressed. A significant enhancement is observed in both velocity profile and stream function by increasing the electroosmotic parameter. Significances and applications: The study is relevant to nano-cooling systems, drug delivery systems, and microfluidic pumps where laminar flows in convergent domains arise. This model is significant for the thermal enhancement of mechanical and chemical rheological processes

A quantitative prediction of the viscosity of amine based DESs using Sσ-profile molecular descriptors

International audienceIn recent years, the preparation of deep eutectic solvents (DESs) using amines as hydrogen bond donors (HBD) has been reported by several research groups. One of the potential use of this type of DESs is in the field of CO2 capture, where the viscosity of the solvent before and after the absorption is of paramount importance. Since the number of possible combinations of DESs is huge, a mathematical model for the predicting of the viscosity of DESs at different temperatures is very important.In this work, a new mathematical model for the prediction of amine-based DESs viscosities using the quantitative structure property relationships (QSPR) approach is presented. A combination of multilinear regression (MLR) and artificial neural networks (ANN) methods is used for the development of the model. A data set of 108 experimental measurements of viscosity of five amines-based DESs, taken from the literature, is used for the development and subsequent verification of the model. The more appropriate model is determined by a dedicated statistical analysis, in which the most significant descriptors are preliminary determined. The results show that the proposed models are able to predict the DESs viscosities with very high accuracy, i.e. with a R2 value of 0.9975 in training and 0.9863 for validation using the ANN model and R2 value of 0.9305 for the MLR model. The retrieved model can be considered as a very reliable tool for the prediction of DESs viscosity when experimental data are absent. In turn, this can provide useful guidelines for the synthesis of low-viscosity DESs able to minimize energy requirements associated to their processing (e.g. power required for pumps), thus fostering their industrial-scale implementation

Study of the dark fermentative hydrogen production using modified ADM1 models

International audienceThis paper presents a numerical study of the fermentation pathway of a bacterial consortium during production of hydrogen from glucose using three kinetic models. Mathematical expressions were used to describe glucose consumption, microbial growth and the production of hydrogen and metabolites. The numerical results show a good agreement with the experimental data. Microbial growth was described using three different kinetic models. A correction factor (CF) was added to the “Anaerobic Digestion Model number 1” (ADM1) using the Modified Model Aiba Kinetic model (MMAK-CF), allowing to improve the model agreement. Different initial concentrations of substrate were used to study their effect on hydrogen production and bacterial growth. The results show that the cumulative produced hydrogen increases with increasing the substrate concentration to reach a maximum. An optimal value was calculated at the initial substrate concentration of 0.022 mol L−1. Bacterial growth followed the same trend. It was concluded that high substrate concentrations inhibit bacterial growth and hydrogen production, which can distort the metabolism of microorganisms