Flunisolide

As a potent antioxidant, the flavonoid compound quercetin (QUE) has been widely used in the farming of aquatic animals. However, there are fewer reports of the beneficial effects, especially in improving immunity of Penaeus vannamei by QUE. The aim of this study was to investigate the effects of dietary QUE on growth, apoptosis, antioxidant and immunity of P. vannamei. It also explored the potential mechanisms of QUE in improving the growth and immunity of P. vannamei. P. vannamei were fed diets with QUE for 60 days. The results revealed that QUE (0.5 or 1.0 g/kg) ameliorated the growth, and the expressions of genes related to apoptosis, antioxidant, and immunity. The differentially expressed genes (DEGs) and differential metabolites (DMs) obtained through transcriptomics and metabolomics, respectively, enriched in pathways related to nutritional metabolism such as lipid metabolism, amino acid metabolism, and carbohydrate metabolism. After QUE addition, especially at 0.5 g/kg, DEGs were enriched into the functions of response to stimulus and antioxidant activity, and the pathways of HIF-1 signaling pathway, C-type lectin receptor signaling pathway, Toll-like receptor signaling pathway, and FoxO signaling pathway. In conclusion, dietary QUE can ameliorate growth, apoptosis, antioxidant and immunity of P. vannamei, the appropriate addition amount was 0.5 g/kg rather than 1.0 g/kg. Regulations of QUE on nutrient metabolism and immune-related pathways, and bioactive metabolites, were important factors for improving the aforementioned abilities in P. vannamei.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.