RESUMEN
Background: Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease, and it carries a poor prognosis due to a lack of efficient diagnosis methods and treatments. Epithelial-mesenchymal transition (EMT) plays a key role in IPF pathogenesis. Endoplasmic reticulum (ER) stress contributes to fibrosis via EMT-mediated pathways. Mesenchymal stem cell (MSC) transplantation is a promising treatment strategy for pulmonary fibrosis and ameliorates lung fibrosis in animal models via paracrine effects. However, the specific mechanisms underlying the effect of transplanted MSCs are not known. We previously reported that MSCs attenuate endothelial injury by modulating ER stress and endothelial-to-mesenchymal transition. The present study investigated whether modulation of ER stress- and EMT-related pathways plays essential roles in MSC-mediated alleviation of IPF. Methods and Results: We constructed a A549 cell model of transforming growth factor-ß1 (TGF-ß1)-induced fibrosis. TGF-ß1 was used to induce EMT in A549 cells, and MSC coculture decreased EMT, as indicated by increased E-cadherin levels and decreased vimentin levels. ER stress participated in TGF-ß1-induced EMT in A549 cells, and MSCs inhibited the expression of XBP-1s, XBP-1u, and BiP, which was upregulated by TGF-ß1. Inhibition of ER stress contributed to MSC-mediated amelioration of EMT in A549 cells, and modulation of the IRE1α-XBP1 branch of the ER stress pathway may have played an important role in this effect. MSC transplantation alleviated bleomycin (BLM)-induced pulmonary fibrosis in mice. MSC treatment decreased the expression of ER stress- and EMT-related genes and proteins, and the most obvious effect of MSC treatment was inhibition of the IRE1α/XBP1 pathway. Conclusions: The present study demonstrated that MSCs decrease EMT by modulating ER stress and that blockade of the IRE1α-XBP1 pathway may play a critical role in this effect. The current study provides novel insight for the application of MSCs for IPF treatment and elucidates the mechanism underlying the preventive effects of MSCs against pulmonary fibrosis.
RESUMEN
Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.
