Integrating UPLC-MS/MS with in Silico and in Vitro Screening Accelerates the Discovery of Active Compounds in Stephania epigaea.

Traditional Chinese medicines (TCMs) are popular in clinic because of their safety and efficacy. They contain abundant natural active compounds, which are important sources of new drug discovery. However, how to efficiently identify active compounds from complex ingredients remains a challenge. In this study, a method combining UHPLC-MS/MS characterization and in silico screening was developed to discover compounds with dopamine D2 receptor (D2R) activity in Stephania epigaea (S. epigaea). By combining the compounds identified in S. epigaea by UHPLC-MS/MS with reported compounds, a virtual library of 80 compounds was constructed for in silico screening. Potentially active compounds were chosen based on screening scores and subsequently tested for in vitro activity on a transfected cell line CHO-K1-D2 model using label-free cellular phenotypic assay. Three D2R agonists and five D2R antagonists were identified. (-)-Asimilobine, N-nornuciferine and (-)-roemerine were reported for the first time as D2R agonists, with EC50 values of 0.35 ± 0.04 µM, 1.37 ± 0.10 µM and 0.82 ± 0.22 µM, respectively. Their target specificity was validated by desensitization and antagonism assay. (-)-Isocorypalmine, (-)-tetrahydropalmatine, (-)-discretine, (+)-corydaline and (-)-roemeroline showed strong antagonistic activity on D2R with IC50 values of 92 ± 9.9 nM, 1.73 ± 0.13 µM, 0.34 ± 0.02 µM, 2.09 ± 0.22 µM and 0.85 ± 0.08 µM, respectively. Their kinetic binding profiles were characterized using co-stimulation assay and they were both D2R competitive antagonists. We docked these ligands with human D2R crystal structure and analyzed the structure-activity relationship of aporphine-type D2R agonists and protoberberine-type D2R antagonists. These results would help to elucidate the mechanism of action of S. epigaea for its analgesic and sedative efficacy and benefit for D2R drug design. This study demonstrated the potential of integrating UHPLC-MS/MS with in silico and in vitro screening for accelerating the discovery of active compounds from TCMs.

The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography.

BACKGROUND: Predicting Parkinson's disease (PD) can provide patients with targeted therapies. However, disease severity can be roughly evaluated in clinical practice based on the patient's symptoms and signs. OBJECTIVE: The current study attempted to explore the factors linked with PD severity and construct a predictive model. METHOD: The PD patients and healthy controls were recruited from our study center while recording their basic demographic information. The serum inflammatory markers levels, such as Cystatin C (Cys C), C-reactive protein (CRP), RANTES (regulated on activation, normal T cell expressed and secreted), Interleukin-10 (IL-10), and Interleukin-6 (IL-6) were determined for all the participants. PD patients were categorized into early and mid-advanced groups based on the Hoehn and Yahr (H-Y) scale and evaluated using PD-related scales. LASSO logistic regression analysis (Model C) helped select variables based on clinical scale evaluations, serum inflammatory factor levels, and transcranial sonography measurements. The optimal harmonious model coefficient λ was determined via 10-fold cross-validation. Moreover, Model C was compared with multivariate (Model A) and stepwise (Model B) logistic regression. The area under the curve (AUC) of a receiver operator characteristic (ROC), brier score, calibration curve, and decision curve analysis (DCA) helped determine the discrimination and calibration of the predictive model, followed by configuring a forest plot and column chart. RESULTS: The study included 113 healthy individuals and 102 PD patients, with 26 early and 76 mid-advanced patients. Univariate analysis of variance screened out statistically significant differences among inflammatory markers Cys C and RANTES. The average Cys C level in the mid-advanced stage was significantly higher than in the early stage (p < 0.001) but not for RANTES (p = 0.740). The LASSO logistic regression model (λ.1se = 0.061) associated with UPDRS-I, UPDRS-II, UPDRS-III, HAMA, PDQ-39, and Cys C as the included independent variables revealed that the Model C discrimination and calibration (AUC = 0.968, Brier = 0.049) were superior to Model A (AUC = 0.926, Brier = 0.079) and Model B (AUC = 0.929, Brier = 0.071) models. CONCLUSION: The study results show multiple factors are linked with PD assessment. Moreover, the inflammatory marker Cys C and transcranial sonography measurement could objectively predict PD symptom severity, helping doctors monitor PD evolution in patients while targeting interventions.

An integrated strategy for the discovery of quality marker of Dactylicapnos scandens based on phytochemical analysis, network pharmacology and activity screening.

Dactylicapnos scandens (D. scandens) is an ethnic medicine commonly used for the treatment of analgesia. In this study, an integrated strategy was proposed for the quality evaluation of D. scandens based on "phytochemistry-network pharmacology-effectiveness-specificity" to discover and determine the quality marker (Q-marker) related to analgesia. First, phytochemical analysis was conducted using UPLC-Q-TOF-MS/MS and a self-built compound library, and 19 components were identified in D. scandens extracts. Next, the "compounds-targets" network was constructed to predict the relevant targets and compounds related to analgesia. Then, the analgesic activity of related compounds was verified through dynamic mass redistribution (DMR) assays on D2 and Mu receptors, and 5 components showed D2 antagonistic activity with IC50 values of 39.2 ± 14.7 µM, 5.46 ± 0.37 µM, 17.5 ± 1.61 µM, 7.89 ± 0.79 µM and 3.29 ± 0.73 µM, respectively. Subsequently, nine ingredients were selected as Q-markers in consideration of specificity, effectiveness and measurability, and their content was measured in 12 batches of D. scandens. Furthermore, the hierarchical cluster analysis and heatmap results indicated that the selected Q-marker could be used to discriminate D. scandens and that the content of Q-marker varied greatly in different batches. Our study shows that this strategy provides a useful method to discover the potential Q-markers of traditional Chinese medicine and offers a practical workflow for exploring the quality consistency of medicinal materials.