Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

Development of Coordinated Methodologies for Modeling CO2-Containing Systems in Petroleum Industry.

Masters Degree. University of KwaZulu-Natal, Durban.Clathrate hydrates formation in natural gas processing facilities or transportation pipelines may lead to process and/or safety hazards. On the other hand, a number of applications are suggested on the basis of promoting the gas hydrate formation. Some researchers have investigated separation and purification processes through gas hydrate crystallization technology. Some works report that the hydrate formation is applicable to the gas transportation and storage. Gas hydrate concept is also studied as a potential method for CO2 capture and/or sequestration. Water desalination/sweetening, and refrigeration and air conditioning systems are other proposed uses of hydrates phenomenon. In the realm of food processing and engineering, several studies have been done investigating the application of gas hydrate technology as an alternative to the conventional processes. Accurate knowledge of phase equilibria of clathrate hydrates is crucial for preventing or utilizing the hydrates. It is believed that energy production or extraction from different fossil fuels is responsible for considerable emissions of CO2, as an important greenhouse gas, into the atmosphere. Furthermore, CO2 removal from the streams of natural gas is important for enhancing the gaseous streams’ heating value. Employment of solvent-based processes and technologies for removing the CO2 is a widely employed approach in practical applications. Amine-based or pure amine solutions are the most common choice to remove the produced CO2 in numerous carbon capture systems. Further to the above, ionic liquids (ILs) are capable to be utilized to capture CO2 from industrial streams. Other potential solvent are sodium piperazine (PZ) and glycinate (SG) solutions. Equilibrium absorption of carbon dioxide in the aqueous phase is a key parameter in any solvent-based CO2 capture process designing. The captured CO2, then, can be injected into the hydrocarbon reservoirs. In addition to the fact that injection of CO2 into potential sources is one of the most reliable methodologies for enhanced hydrocarbon recovery, utilizing this process in conjunction with the CO2 capture systems mitigates the greenhouse effects of CO2. One of the most significant variables determining the success of CO2 injection is known to be the minimum miscibility pressure (MMP) of CO2-reservoir oil. This research study concerns implementation of computer-based methodologies called artificial neural networks (ANNs), classification and regression trees (CARTs)/AdaBoost-CART, adaptive neuro-fuzzy inference systems (ANFISs) and least squares support vector machines (LSSVMs) for modeling: (a) phase equilibria of clathrate hydrates in: 1- pure water, 2- aqueous solutions of salts and/or alcohols, and 3- ILs, (b) phase equilibria (equilibrium) of hydrates of methane in ILs; (c) equilibrium absorption of CO2 in amine-based solutions, ILs, PZ solutions, and SG solutions; and (d) MMP of CO2-reservoir oil. To this end, related experimental data have been gathered from the literature. Performing error analysis, the performance of the developed models in representing/ estimating the independent parameter has been assessed. For the studied hydrate systems, the developed ANFIS, LSSVM, ANN and AdaBoost-CART models show the average absolute relative deviation percent (AARD%) of 0.04-1.09, 0.09-1.01, 0.05-0.81, and 0.03-0.07, respectively. In the case of hydrate+ILs, error analysis of the ANFIS, ANN, LSSVM, and CART models showed 0.31, 0.15, 0.08, and 0.10 AARD% of the results from the corresponding experimental values. Employing the collected experimental data for carbon dioxide (CO2) absorption in amine-based solutions, the presented models based on ANFIS, ANN, LSSVM, and AdaBoost-CART methods regenerated the targets with AARD%s between 2.06 and 3.69, 3.92 and 8.73, 4.95 and 6.52, and 0.51 and 2.76, respectively. For the investigated CO2+IL systems, the best results were obtained using CART method as the AARD% found to be 0.04. Amongst other developed models, i.e. ANN, ANFIS, and LSSVM, the LSSVM model provided better results (AARD%=17.17). The proposed AdaBoost-CART tool for the CO2+water+PZ system reproduced the targets with an AARD% of 0.93. On the other hand, LSSVM, ANN, and ANFIS models showed AARD% values equal to 16.23, 18.69, and 15.99, respectively. Considering the CO2+water+SG system, the proposed AdaBoost-CART tool correlated the targets with a low AARD% of 0.89. The developed ANN, ANFIS, and LSSVM showed AARD% of more than 13. For CO2-oil MMP, the proposed AdaBoost-CART model (AARD%=0.39) gives better estimations than the developed ANFIS (AARD%=1.63). These findings revealed the reliability and accuracy of the CART/AdaBoost-CART methodology over other intelligent modeling tools including ANN, ANFIS, and LSSVM

Intelligent Modelling of the Environmental Behaviour of Chemicals

In view of the new European Union chemical policy REACH (Registration, Evaluation, and Authorization of Chemicals), interest in "non-animal" methods for assessing the risk potentials of chemicals towards human health and environment has increased. The incapability of classical modelling approaches in the complex and ill-defined modelling problems of chemicals' environmental behavior, together with an availability of large computing power in modern times raise an interest in applying computational models inspired by the approaches coming from the area of artificial intelligence. This thesis is devoted to promote the applications of neuro/fuzzy techniques in assessing the environmental behavior of chemicals. Some of the bottlenecks lying in the neuro/fuzzy modelling of chemicals' behavior towards environment have been identified and the solutions have been provided based on the techniques of computational intelligence.Diese Dissertation beinhaltet die Anwendung von neuronalen bzw. fuzzy Netzen, um das Umweltverhalten von Chemikalien beurteilen zu können. In dieser Arbeit werden die Probleme der Modellierung von Chemikalien gegenüber der Umwelt aufgezeigt und Lösungen angeboten. Die Lösungen basieren auf künstlichen Intelligenztechniken. Die Qualität der Modellierungstechniken hängt von mehreren Faktoren ab, z.B. der Eingabe, der Struktur und so weiter. In vielen Fällen werden keine geeigneten Resultate erhalten. So läuft es auf die Entwicklung eines Modells mit einer niedrigen Generalisierungsfähigkeit (Verallgemeinerungsfähigkeit)hinaus

Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system

Se pretende obtener un material semibiodegradable basado en ácido hialurónico químicamente enlazado a cadenas de polímeros acrílicos. Los hidrogeles de ácido hialurónico presentan en general buenas características para su utilización en regeneración del sistema nervioso central: es biodegradable, es un componente importante del tejido neural, sus propiedades mecánicas son semejantes a las del tejido cerebral, promueve la formación de nuevos capilares (angiogénesis), y limita la inflamación. Con este nuevo material se pretende mejorar el excesivo grado de hinchado en medio fisiológico, su rápida degradación, mejorar la adhesión celular, además la matriz permanente de las cadenas acrílicas pueden actuar como un soporte permanente durante el proceso regenerativo sin que se produzca una pérdida brusca de propiedades mecánicas y estructurales. El trabajo consiste en caracterizar este nuevo material así como los productos intermedios necesarios para su obtención final, comparándolo con las propiedades de un hidrogel de ácido hialurónico sin incorporar cadenas acrílicas. Los estudios celulares se llevaran a cabo in vitro, como fase preliminar para futuros implantes en el cortex cerebral, estudiando la capacidad de diferenciación de precursores neurales y de generación de nuevos capilares con el fenotipo típico de la barrera hematoencefálica, mediante el estudio de cocultivos de precursores neurales y células endoteliales.Perez Garnes, M. (2015). Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4879