Fibrosis Quística: tratamiento actual y avances con la nanotecnología / Cystic Fibrosis: actual treatment and future perspectives with nanotechnology

INTRODUCCIÓN: Actualmente, los tratamientos existentes para tratar la fibrosis quística (FQ) están diseñados para controlar sus síntomas, consistentes principalmente en retención de moco e infección crónica. Se propone la vía pulmonar como alternativa para la administración de los fármacos, principalmente antimicrobianos. Sin embargo, su rápido aclaramiento, que conduce a niveles bajos de fármaco e incremento de los regímenes posológicos, así como la aparición de efectos adversos, hacen de la nanotecnología una estrategia interesante para esta enfermedad. OBJETIVO: estudiar y analizar los diferentes sistemas nanoparticulares existentes para su uso por vía pulmonar, concretando en el uso de sistemas lipídicos para el tratamiento de la FQ. MÉTODO: se realizó una búsqueda no sistemática de artículos en diferentes bases de datos, en los últimos 10 años principalmente, siguiendo pautas establecidas de palabras clave. RESULTADOS: Los progresos que se han conseguido en los últimos años hacen que la FQ pase a ser una enfermedad de adultos. Los tratamientos que se están usando en la actualidad están siendo cada vez más desplazados por otras alternativas, como los sistemas nanoparticulares, siendo idóneos para la administración pulmonar debido a su pequeño tamaño, su liberación sostenida y su elevada biocompatibilidad. Entre éstos, destacan los liposomas por su similitud estructural con el surfactante pulmonar, así como por su capacidad de destruir las biopelículas bacterianas. La mayoría de las formulaciones encontradas contenían un solo fármaco. CONCLUSIÓN: Existen evidencias científicas que indican que la investigación debe dirigirse hacia el desarrollo de formulaciones que sean capaces de destruir la biopelícula

INTRODUCTION: Currently, the management of treatments in cystic fibrosis (CF) is mainly focused to control symptoms, which consist of mucus retention and chronic infection. The pulmonary route is proposed as an interesting alternative for administering drugs, especially antimicrobials. However, the rapid clearance of these, which leads to low drug levels and increased dosage regimens, as well as the appearance of adverse effects, make nanotechnology an interesting strategy for this disease. OBJECTIVE: to study and analyze the different nanoparticulate systems available for use via the lung, specifying the use of lipid systems for the treatment of CF. Method: a non-systematic search of articles in different databases was carried out, mainly in the last 10 years, following established guidelines for selecting keywords. RESULTS: the progress in recent years makes CF become an adult disease. Current treatments are increasingly being displaced by other alternatives, such as nanoparticular systems, being suitable for pulmonary administration due to their small size, sustained release and high biocompatibility. Among these, liposomes stand out for their structural similarity to lung surfactant, as well as for their ability to destroy bacterial biofilms. Most of the formulations contained a single drug. CONCLUSIONS: Scientific literature evidenced that research studies should be directed towards the development of formulations that are intended to destroy the biofilm

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.