Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Network pharmacology‒based analysis of marine cyanobacteria derived bioactive compounds for application to Alzheimer's disease

In recent years, the Alzheimer's disease (AD) epidemic has become one of the largest global healthcare crises. Besides, the available systemic therapies for AD are still inadequate. Due to the insufficient therapeutic options, new treatment strategies are urgently needed to achieve a satisfactory therapeutic effect. Marine bio-resources have been accepted as one of the most economically viable and sustainable sources with potential applications for drug discovery and development. In this study, a marine cyanobacteria-Synechococcus sp. XM-24 was selected as the object of research, to systematically investigate its therapeutic potential mechanisms for AD. The major active compounds derived from the Synechococcus sp. biomass were identified via pyrolysis-gas chromatography-mass spectrometry (GC-MS), and 22 compounds were identified in this strain. The most abundant chemical compounds was (E)-octadec-11-enoic acid, with the peak area of 30.6%. Follow by tridecanoic acid, 12-methyl- and hexadecanoic acid, with a peak area of 23.26% and 18.23%, respectively. GC-MS analysis also identified indolizine, isoquinoline, 3,4-dihydro- and Phthalazine, 1-methyl-, as well as alkene and alkane from the strain. After the chemical toxicity test, 10 compounds were finally collected to do the further analysis. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Based on the analysis, the 10 Synechococcus-derived active compounds could interact with 128 related anti-AD targets. Among them, epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA) and mitogen-activated protein kinase 3 (MAPK3) were the major targets. Furthermore, the compounds N-capric acid isopropyl ester, (E)-octadec-11-enoic acid, and 2H-Pyran-2,4(3H)-dione, dihydro-6-methyl- obtained higher degrees in the compounds-intersection targets network analysis, indicating these compounds may play more important role in the process of anti-AD. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these active compounds exert the anti-AD effects mainly through PI3K-Akt signaling pathway, neuroactive ligand-receptor interaction and ras signaling pathway. Our study identified Synechococcus-derived bioactive compounds have the potential for application to AD by targeting multiple targets and related pathways, which will provide a foundation for future research on applications of marine cyanobacteria in the functional drug industry.